WO2019000961A1 - 一种基于算法的音频优化方法、智能终端及存储装置 - Google Patents

一种基于算法的音频优化方法、智能终端及存储装置 Download PDF

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WO2019000961A1
WO2019000961A1 PCT/CN2018/076101 CN2018076101W WO2019000961A1 WO 2019000961 A1 WO2019000961 A1 WO 2019000961A1 CN 2018076101 W CN2018076101 W CN 2018076101W WO 2019000961 A1 WO2019000961 A1 WO 2019000961A1
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frequency
audio
mapping function
algorithm
optimization method
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French (fr)
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陈学思
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Shenzhen Chuangwei RGB Electronics Co Ltd
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Shenzhen Chuangwei RGB Electronics Co Ltd
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Priority to EP18814481.0A priority Critical patent/EP3471097B1/en
Priority to US16/319,156 priority patent/US10861472B2/en
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G10L19/0204—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders using subband decomposition
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/003—Changing voice quality, e.g. pitch or formants
    • G10L21/007—Changing voice quality, e.g. pitch or formants characterised by the process used
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0316—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude
    • G10L21/0364—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude for improving intelligibility
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
    • G10H1/00—Details of electrophonic musical instruments
    • G10H1/02—Means for controlling the tone frequencies, e.g. attack or decay; Means for producing special musical effects, e.g. vibratos or glissandos
    • G10H1/06—Circuits for establishing the harmonic content of tones, or other arrangements for changing the tone colour
    • G10H1/12—Circuits for establishing the harmonic content of tones, or other arrangements for changing the tone colour by filtering complex waveforms
    • G10H1/125—Circuits for establishing the harmonic content of tones, or other arrangements for changing the tone colour by filtering complex waveforms using a digital filter
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G10L19/028—Noise substitution, i.e. substituting non-tonal spectral components by noisy source
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G10L19/032—Quantisation or dequantisation of spectral components
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L21/0232—Processing in the frequency domain
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03G—CONTROL OF AMPLIFICATION
    • H03G5/00—Tone control or bandwidth control in amplifiers
    • H03G5/16—Automatic control
    • H03G5/165—Equalizers; Volume or gain control in limited frequency bands
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03G—CONTROL OF AMPLIFICATION
    • H03G9/00—Combinations of two or more types of control, e.g. gain control and tone control
    • H03G9/02—Combinations of two or more types of control, e.g. gain control and tone control in untuned amplifiers
    • H03G9/025—Combinations of two or more types of control, e.g. gain control and tone control in untuned amplifiers frequency-dependent volume compression or expansion, e.g. multiple-band systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers
    • H04R3/04—Circuits for transducers for correcting frequency response
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE
    • G10H2250/00—Aspects of algorithms or signal processing methods without intrinsic musical character, yet specifically adapted for or used in electrophonic musical processing
    • G10H2250/131—Mathematical functions for musical analysis, processing, synthesis or composition
    • G10H2250/215—Transforms, i.e. mathematical transforms into domains appropriate for musical signal processing, coding or compression
    • G10H2250/235—Fourier transform; Discrete Fourier Transform [DFT]; Fast Fourier Transform [FFT]
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS
    • H04R2430/00—Signal processing covered by H04R, not provided for in its groups
    • H04R2430/03—Synergistic effects of band splitting and sub-band processing

Definitions

  • the present invention relates to the field of audio processing technologies, and in particular, to an algorithm-based audio optimization method, an intelligent terminal, and a storage device.
  • the audio processing in television programs is mainly to improve the frequency response of the audio device, so that the frequency response curve reaches a "flat" effect to directly reflect the sound reproduction capability, in order to pursue the accuracy of the sound.
  • Different products have different responsiveness to different audio frequencies. If the frequency response curve is flatter, the sound reproduction is better.
  • the sense of human hearing has a great relationship with the sound of each frequency band.
  • the amplitudes and distributions of the various frequency bands are different, and the feelings enjoyed by the listeners are different. For example, if the audio contains excessive low-order harmonics, it is easy to cause fatigue in the human ear; music playing audio such as the Western Orchestra can increase the volume of about 8 kHz, which can increase the brightness.
  • the existing methods for improving the audio characteristics are to use a frequency equalizer to filter the audio waveform and adjust the gain values of the respective frequency bands.
  • the frequency equalizer can improve the auditory effect of music, but this method is only suitable for manual adjustment of personal preference style, and can not automatically adjust the sound effect under different conditions, which can not bring better hearing to the general audience at any time. effect.
  • the technical problem to be solved by the present invention is to provide an algorithm-based audio optimization method, an intelligent terminal and a storage device aiming at the above-mentioned defects of the prior art, aiming to find similar sound source types by comparing and matching original audio with standard audio sources. Then, the frequency mapping function of the transformation is determined, the frequency is mapped, the relevant frequency is compressed or expanded, and the tuning is automatically performed to improve the sound quality.
  • An algorithm based audio optimization method wherein the method comprises:
  • the processed audio file is converted by the function library matching corresponding frequency mapping function, and then the optimized audio file is obtained by inverse Fourier transform.
  • the algorithm-based audio optimization method wherein the function library includes a first frequency mapping function and a second frequency mapping function, and the first frequency mapping function and the second frequency mapping function are used to pass an audio frequency distribution range Make targeted changes and adjust the audio frequency.
  • the algorithm-based audio optimization method wherein the first frequency mapping function and the second frequency mapping function are used to perform a targeted change on an audio frequency distribution range, including:
  • the targeted processing is performed by selecting the first frequency mapping function or the second frequency mapping function.
  • the algorithm-based audio optimization method wherein the first frequency mapping function is:
  • f is the frequency of the audio in the audio file in the frequency domain and f' is the frequency of the audio in the processed audio file.
  • the algorithm-based audio optimization method wherein the frequency conversion processing of the audio in the audio file on the frequency domain by the first frequency mapping function is:
  • a frequency greater than or equal to 1000 Hz in the audio file on the frequency domain is converted to 1000 Hz for erasing the high-pitched portion of the audio.
  • f is the frequency of the audio in the audio file in the frequency domain
  • f' is the frequency of the audio in the processed audio file
  • k is the expansion factor
  • the algorithm-based audio optimization method wherein the second frequency mapping function processes frequency conversion of audio in an audio file on a frequency domain as:
  • a frequency greater than 330 Hz in the audio file on the frequency domain is k-scaled.
  • the algorithm-based audio optimization method wherein the first frequency mapping function is used to preserve an intermediate frequency portion of an audio, compress a low frequency and a high frequency distribution range, and is applied to a language program that reduces background noise and keeps the sound clear and pure;
  • the second frequency mapping function is used to extend the mid-range and high-pitched portions of the audio, compress the bass portion, and is applied to a metal music performance that emphasizes the mid-pitched high-pitched sound to represent the metal striking sound.
  • the algorithm-based audio optimization method wherein the adjustment points of the frequency band are drawn as: 330 Hz and 1000 Hz, less than 330 Hz is classified as a low frequency, greater than 330 Hz is less than 1000 Hz as an intermediate frequency, and greater than 1000 Hz is a high frequency.
  • the algorithm-based audio optimization method wherein the first frequency mapping function converts all frequencies less than or equal to 330 Hz to 330 Hz, and erases the bass portion of the audio; the original frequency is greater than 330 Hz but less than 1000 Hz.
  • the frequency of 1000 Hz or more is adjusted to 1000 Hz, and the high frequency information disappears.
  • the algorithm-based audio optimization method wherein the second frequency mapping function erases an audio frequency less than or equal to 330 Hz, expands an audio frequency greater than 330 Hz, expands a frequency range greater than 330 Hz, and expands a midrange and The treble part compresses the bass.
  • the algorithm-based audio optimization method wherein the frequency mapping function in the function library improves the sound quality performance of the audio by performing a targeted change on the audio frequency distribution range, and the function of the function library is used to implement the audio file (F[y] (t)]) to the transformation of the audio file (F'[y(t)]), transform into linear transformation, nonlinear transformation, piecewise linear transformation or piecewise nonlinear transformation; the expression of the function is divided according to the frequency band Different segment representations.
  • the algorithm-based audio optimization method wherein frequency and frequency are mapped, and the correlation frequency is compressed or expanded by a frequency mapping function.
  • An intelligent terminal comprising: a processor, a memory communicatively coupled to the processor, the memory storing a computer program for implementing the algorithm-based audio optimization method when executed;
  • a processor is operative to invoke a computer program in the memory to implement the algorithm-based audio optimization method.
  • a storage device wherein the storage device stores a computer program that can be executed to implement the algorithm-based audio optimization method.
  • the present invention discloses an algorithm-based audio optimization method, an intelligent terminal, and a storage device, the method comprising: converting a raw audio file in a time domain into a frequency domain by Fourier transform in advance Audio file; extracting the frequency range and frequency amplitude information of the audio signal, matching the frequency range and frequency amplitude information of the existing different types of audio test standard sound sources, determining the type of the audio signal; according to the type of the audio signal, passing the function
  • the library matches the corresponding frequency mapping function to convert the processed audio file, and then obtains the optimized audio file by inverse Fourier transform.
  • the invention compares the original audio with the standard sound source, finds the similar sound source type, determines the transformed frequency mapping function, maps the frequency, compresses or expands the relevant frequency, and automatically performs tuning to achieve the effect of improving the sound quality.
  • FIG. 1 is a flow chart of a preferred embodiment of an algorithm based audio optimization method of the present invention.
  • FIG. 2 is a flow chart of a preferred embodiment of the present invention for performing a targeted change process of an audio frequency distribution range by a frequency mapping function.
  • FIG. 3 is a flow chart of a preferred embodiment of frequency conversion processing of audio in an audio file over a frequency domain by a first frequency mapping function in an algorithm-based audio optimization method of the present invention.
  • FIG. 4 is a flow chart of a preferred embodiment of frequency conversion processing of audio in an audio file over a frequency domain by a second frequency mapping function in an algorithm-based audio optimization method of the present invention.
  • FIG. 5 is a functional block diagram of a preferred embodiment of a mobile terminal in an algorithm-based audio optimization method according to the present invention.
  • An algorithm-based audio optimization method includes the following steps:
  • Step S100 Convert the original audio file in the time domain into an audio file on the frequency domain by Fourier transform in advance.
  • the audio data in the original audio file describes the relationship of the amplitude change of the sound over time.
  • the time domain is required (the time domain is a description of a mathematical function or a physical signal pair).
  • the relationship of time such as the time domain waveform of a signal can express the signal over time
  • the original audio file (y(t)) is converted to the frequency domain by Fourier transform (the frequency domain is a description of the frequency characteristics of the signal)
  • the Fourier transform is a very important algorithm in the field of digital signal processing.
  • the Fourier principle shows that any continuous measurement of timing or signal can be expressed as an infinite superposition of sinusoidal signals of different frequencies, and is created according to this principle.
  • the Fourier transform algorithm uses the directly measured raw signal to calculate the frequency, amplitude and phase of different sinusoidal signals in the signal in an additive manner. Therefore, it can be said that the Fourier transform converts the time domain signal that was originally difficult to process into a frequency domain signal (signal spectrum) that is easy to analyze, and some of the frequency domain signals can be processed and processed by some tools. Finally, these frequency domain signals can also be converted into time domain signals using an inverse Fourier transform.
  • the Fourier transform formula is:
  • F( ⁇ ) is an image function of f(t)
  • f(t) is an image-like function of F( ⁇ ).
  • Step S200 Extracting a frequency range and frequency amplitude information of the audio signal, matching frequency range and frequency amplitude information of the existing different types of audio test standard sound sources, and determining the type of the audio signal.
  • the audio file (F[y(t)]) in the frequency domain into a processed audio file (F'[y(t)]) with better sound quality it is necessary to select an appropriate frequency from the function library.
  • the frequency range of various sound sources is different.
  • the frequency range of the grand piano is 27 Hz to 12000 Hz
  • the footstep sound is 100 Hz to 9000 Hz.
  • the selection of its frequency mapping function can be based on a standard source as the matching information standard.
  • the frequency range and frequency amplitude information of the audio signal are extracted, and the frequency range and the frequency amplitude information of the existing different types of audio test standard sound sources are matched. Find the standard audio source that matches the audio test, that is, determine the type of the audio signal to determine the frequency mapping function to be transformed.
  • Step S300 according to the type of the audio signal, the processed audio file is converted by the function library matching corresponding frequency mapping function, and then the optimized audio file is obtained by inverse Fourier transform.
  • the function library includes a first frequency mapping function and a second frequency mapping function, where the first frequency mapping function and the second frequency mapping function are used to adjust an audio frequency by performing a targeted change on an audio frequency distribution range. To improve the sound quality of the audio.
  • the first frequency mapping function and the second frequency mapping function are used to perform targeted changes to the audio frequency distribution range, including:
  • the frequency band of the audio in the audio file in the frequency domain is divided into a plurality of intervals in advance; for example, two intervals or three intervals are different;
  • the targeted processing is performed by selecting the first frequency mapping function or the second frequency mapping function.
  • the first frequency mapping function is:
  • f is the frequency of the audio in the audio file in the frequency domain and f' is the frequency of the audio in the processed audio file.
  • the first frequency mapping function processes the frequency conversion of the audio in the audio file in the frequency domain as (ie, the specific meaning of the first frequency mapping function formula):
  • the present invention divides the adjustment point of the frequency band into: 330 Hz, 1000 Hz, that is, less than 330 Hz is classified as a low frequency, and greater than 330 Hz is less than 1000 Hz as an intermediate frequency, and greater than 1000 Hz is a high frequency.
  • the first frequency mapping function converts all frequencies less than or equal to 330 Hz to 330 Hz, so that the bass portion of the audio is erased; the original frequency is greater than 330 Hz but less than 1000 Hz, and the frequency greater than or equal to 1000 Hz is adjusted to 1000 Hz. That is, the high frequency information disappears.
  • This function preserves the IF section and compresses the low frequency and high frequency distribution range. It is mainly used for sound sources such as language programs. The purpose is to reduce low frequency and high frequency components, which can reduce background noise and clear and pure sound.
  • the second frequency mapping function is:
  • f is the frequency of the audio in the audio file in the frequency domain
  • f' is the frequency of the audio in the processed audio file
  • k is the expansion factor
  • the second frequency mapping function processes the frequency conversion of the audio in the audio file in the frequency domain as (ie, the specific meaning of the second frequency mapping function formula):
  • the second frequency mapping function of the present invention erases an audio frequency less than or equal to 330 Hz, expands an audio frequency greater than 330 Hz, and expands a frequency range greater than 330 Hz, that is, expands the midrange and high-pitched portions, and compresses the bass.
  • k can also be a compression factor (ie, k ⁇ 1, at which point some of the frequencies in the audio are compressed).
  • the function library in the present invention is preferably composed of a first frequency mapping function and a second frequency mapping function, and may of course include other frequency mapping functions.
  • the frequency mapping function in the function library mainly changes the frequency distribution range of the audio frequency.
  • the function of the function library is used to transform the audio file (F[y(t)]) to the audio file (F'[y(t)]).
  • This transformation can be linear transformation, nonlinear transformation (logarithmic transformation, exponential) Transform) or piecewise linear transformation (such as first frequency mapping function and second frequency mapping function), piecewise nonlinear transformation.
  • the expression of the function can be expressed according to different segments of the frequency band (high frequency, intermediate frequency, low frequency) to achieve fine or simple processing of the audio signal. According to the requirements of professional tuner, the expressions for different sound quality improvements are constantly added.
  • These functions constitute a specialized function library for audio processing. Depending on the application requirements, different transformation functions are selected, which will selectively expand or compress a certain frequency range.
  • the technical scheme of the present invention is different from the traditional filtering to improve the sound quality.
  • the present invention maps the frequency and the frequency, compresses or expands the relevant frequency through the frequency mapping function, modifies the timbre in a targeted manner, achieves the effect of improving the sound quality, and optimizes the auditory effect;
  • the present invention further provides a mobile terminal.
  • the mobile terminal includes: a processor 10, a memory 20, a communication interface 30, and a bus 40;
  • the processor 10, the memory 20, and the communication interface 30 complete communication with each other through the bus 40;
  • the communication interface 30 is used for information transmission between communication devices of the mobile terminal;
  • the processor 10 is configured to invoke a computer program in the memory 20 to perform the method provided by the foregoing method embodiments, for example, including: converting a original audio file in a time domain into a frequency domain by Fourier transform in advance.
  • the processed audio file is converted by the function library matching corresponding frequency mapping function, and then the optimized audio file is obtained by inverse Fourier transform.
  • the present invention also provides a storage device, wherein the storage device stores a computer program executable to implement the algorithm-based audio optimization method.
  • the present invention provides an algorithm-based audio optimization method, an intelligent terminal, and a storage device, the method comprising: converting an original audio file in a time domain into an audio in a frequency domain by Fourier transform in advance. File; extracting the frequency range and frequency amplitude information of the audio signal, matching the frequency range and frequency amplitude information of the existing different types of audio test standard sound sources, determining the type of the audio signal; according to the type of the audio signal, passing the function library Matching the corresponding frequency mapping function converts the processed audio file, and then obtains the optimized audio file by inverse Fourier transform.
  • the invention compares the original audio with the standard sound source, finds the similar sound source type, determines the transformed frequency mapping function, maps the frequency, compresses or expands the relevant frequency, automatically performs tuning, and achieves the effect of improving the sound quality; compensating the sound Demonstrate the defects in human hearing to achieve a better hearing effect.
  • a computer program to instruct related hardware (such as a processor, a controller, etc.), and the program can be stored in one.
  • the program when executed, may include the processes of the various method embodiments as described above.
  • the storage medium described therein may be a memory, a magnetic disk, an optical disk, or the like.

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  • Engineering & Computer Science (AREA)
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  • Acoustics & Sound (AREA)
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  • Multimedia (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
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Abstract

一种基于算法的音频优化方法、智能终端及存储装置，方法包括：预先将时域上的原始音频文件通过傅里叶变换转换为频域上的音频文件（S100)；提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，确定音频信号的类型（S200）；根据音频信号的类型，通过函数库匹配对应的频率映射函数转换得到处理后的音频文件，再通过傅里叶逆变换得到优化的音频文件（S300）。通过原始音频与标准音源的对比匹配，寻找相近的音源类型，进而确定变换的频率映射函数，将频率进行映射，压缩或扩展相关频率，自动进行调音，达到改善音质的效果。

Description

一种基于算法的音频优化方法、智能终端及存储装置 技术领域
本发明涉及音频处理技术领域，尤其涉及一种基于算法的音频优化方法、智能终端及存储装置。
背景技术
目前，电视节目中的音频处理主要是改善音频设备的频率响应，使频率响应曲线达到“平直”效果来直接反应声音的再现能力，以追求声音的准确性。不同产品对不同音频频率的响应能力不同，若其频率响应曲线越平直，声音再现越好。但是人耳听感与各频段声音有很大的关系，各频段的幅度不同和分布不同，给听者带来的感觉享受不同。例如音频若包含过量的低次级谐波，容易使人耳产生疲劳；音乐演奏音频如西洋管弦乐队，提升其8千赫兹左右的音量，可以增加明亮度。所以只追求频响曲线平直、声音准确的处理方法，容易失去声音的特性，声音不能完美表现出来。现有的常用来提高音频特性的方法，是利用频率均衡器对音频波形进行滤波，调整各个频段的增益值。通过频率均衡器可以改善音乐的听觉效果，但这种方法只适用于个人喜好风格的手动调整，且不能自动调整不同条件场景下的音效，对于普通观众来说并不能随时带来更好的听觉效果。
因此，现有技术还有待于改进和发展。
发明内容
本发明要解决的技术问题在于，针对现有技术的上述缺陷，提供一种基于算法的音频优化方法、智能终端及存储装置，旨在通过原始音频与标准音源的对比匹配，寻找相近的音源类型，进而确定变换的频率映射函数，将频率进行映射，压缩或扩展相关频率，自动进行调音，达到改善音质的效果。
本发明解决技术问题所采用的技术方案如下：
一种基于算法的音频优化方法，其中，所述方法包括：
预先将时域上的原始音频文件通过傅里叶变换转换为频域上的音频文件；
提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，确定音频信号的类型；
根据音频信号的类型，通过函数库匹配对应的频率映射函数转换得到处理后的音频文件，再通过傅里叶逆变换得到优化的音频文件。
所述的基于算法的音频优化方法，其中，所述函数库包括第一频率映射函数和第二频率映射函数，所述第一频率映射函数和第二频率映射函数用于通过对音频频率分布范围进行针对性改变，调整音频频率。
所述的基于算法的音频优化方法，其中，所述第一频率映射函数和第二频率映射函数用于通过对音频频率分布范围进行针对性改变具体包括：
预先将所述频域上的音频文件中音频的频段划分为若干个区间；
根据音频信号的类型，对于每个不同区间频段的频率，通过选择第一频率映射函数或者第二频率映射函数进行针对性处理。
所述的基于算法的音频优化方法，其中，所述第一频率映射函数为：
Figure PCTCN2018076101-appb-000001
其中f为频域上的音频文件中音频的频率，f'为处理后的音频文件中音频的频率。
所述的基于算法的音频优化方法，其中，所述第一频率映射函数对频域上的音频文件中音频的频率转换处理为：
将频域上的音频文件中小于等于330Hz的频率转换为330Hz，用于将音频的低音部分擦除；
将频域上的音频文件中大于330Hz但小于1000Hz的频率保持不变；
将频域上的音频文件中大于等于1000Hz的频率转换为1000Hz，用于将音频的高音部分擦除。
所述的基于算法的音频优化方法，其中，所述第二频率映射函数为：
Figure PCTCN2018076101-appb-000002
其中f为频域上的音频文件中音频的频率，f'为处理后的音频文件中音频的频率，k为扩展系数。
所述的基于算法的音频优化方法，其中，所述第二频率映射函数对频域上的音频文件中音频的频率转换处理为：
将频域上的音频文件中小于等于330Hz的频率转换为330Hz，用于将音频的 低音部分擦除；
将频域上的音频文件中大于330Hz的频率进行k倍扩大。
所述的基于算法的音频优化方法，其中，所述第一频率映射函数用于保留音频的中频部分，压缩低频和高频分布范围，应用于减小背景噪音和保持声音清晰纯正的语言节目；
所述第二频率映射函数用于扩展音频的中音和高音部分，压缩低音部分，应用于强调中音高音来表现金属打击声音的金属音乐演奏。
所述的基于算法的音频优化方法，其中，将频段的调整点划为：330Hz和1000Hz，小于330Hz划为低频，大于330Hz小于1000Hz为中频，大于1000Hz为高频。
所述的基于算法的音频优化方法，其中，所述第一频率映射函数将原来所有小于等于330Hz的频率转换为330Hz，将音频的低音部分擦除掉；原大于330Hz但小于1000Hz的频率不变，大于等于1000Hz的频率调整为1000Hz，高频信息消失。
所述的基于算法的音频优化方法，其中，所述第二频率映射函数将小于等于330Hz的音频频率擦除，将大于330Hz的音频频率进行扩大，扩大了大于330Hz的频率范围，扩展中音和高音部分，压缩低音。
所述的基于算法的音频优化方法，其中，函数库中的频率映射函数通过对音频频率分布范围进行针对性改变，来改善音频的音质表现，函数库的函数用来实现音频文件(F[y(t)])到音频文件(F'[y(t)])的变换，变换为线性变换，非线性变换、分段线性变换或者分段非线性变换；函数的表达式根据频段划分点的不同分段表示。
所述的基于算法的音频优化方法，其中，将频率与频率进行映射，通过频率映射函数压缩或扩展相关频率。
一种智能终端，其中，包括：处理器、与处理器通信连接的存储器，所述存储器存储有计算机程序，所述计算机程序用于被执行时实现所述的基于算法的音频优化方法；所述处理器用于调用所述存储器中的计算机程序，以实现所述的基于算法的音频优化方法。
一种存储装置，其中，所述存储装置存储有计算机程序，所述计算机程序能 够被执行用于实现所述的基于算法的音频优化方法。
本发明的有益效果：本发明公开了一种基于算法的音频优化方法、智能终端及存储装置，所述方法包括：预先将时域上的原始音频文件通过傅里叶变换转换为频域上的音频文件；提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，确定音频信号的类型；根据音频信号的类型，通过函数库匹配对应的频率映射函数转换得到处理后的音频文件，再通过傅里叶逆变换得到优化的音频文件。本发明通过原始音频与标准音源的对比匹配，寻找相近的音源类型，进而确定变换的频率映射函数，将频率进行映射，压缩或扩展相关频率，自动进行调音，达到改善音质的效果。
附图说明
图1是本发明基于算法的音频优化方法的较佳实施例的流程图。
图2是本发明通过频率映射函数对音频频率分布范围进行针对性改变过程的较佳实施例的流程图。
图3是本发明基于算法的音频优化方法中通过第一频率映射函数对频域上的音频文件中音频的频率转换处理的较佳实施例的流程图。
图4是本发明基于算法的音频优化方法中通过第二频率映射函数对频域上的音频文件中音频的频率转换处理的较佳实施例的流程图。
图5是本发明基于算法的音频优化方法中移动终端的较佳实施例的功能原理框图。
具体实施方式
为使本发明的目的、技术方案及优点更加清楚、明确，以下参照附图并举实施例对本发明进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
本发明较佳实施例所述的一种基于算法的音频优化方法，如图1所示，所述方法包括以下步骤：
步骤S100、预先将时域上的原始音频文件通过傅里叶变换转换为频域上的音频文件。
具体地，原始音频文件中的音频数据描述的是随时间变化声音幅值变化的关系，由于本发明是对音频的频率进行转换，所以需要把时域(时域是描述数学函 数或物理信号对时间的关系，例如一个信号的时域波形可以表达信号随着时间的变化)上的原始音频文件(y(t))通过傅里叶变换转换为频域(频域是描述信号在频率方面特性时用到的一种坐标系)上的音频文件(F[y(t)])。
傅里叶变换是数字信号处理领域一种很重要的算法，傅里叶原理表明：任何连续测量的时序或信号，都可以表示为不同频率的正弦波信号的无限叠加，而根据该原理创立的傅里叶变换算法利用直接测量到的原始信号，以累加方式来计算该信号中不同正弦波信号的频率、振幅和相位。因此，可以说，傅里叶变换将原来难以处理的时域信号转换成了易于分析的频域信号(信号的频谱)，可以利用一些工具对这些频域信号进行处理、加工。最后还可以利用傅里叶逆变换将这些频域信号转换成时域信号。
傅里叶变换公式为：
Figure PCTCN2018076101-appb-000003
公式中F(ω)为f(t)的像函数，f(t)为F(ω)的像原函数。
步骤S200、提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，确定音频信号的类型。
具体地，要对频域上的音频文件(F[y(t)])处理为音质较好的处理后的音频文件(F'[y(t)])，需要从函数库选择合适的频率映射函数来进行转换。各种声源频率范围不同，例如大钢琴的频率范围为27Hz～12000Hz，脚步声为100Hz～9000Hz。任何一个音频文件，其频率映射函数的选取可以根据一个标准音源作为匹配信息标准。
根据音频信号的某些特性(频率分布范围、频幅信息)，提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，找到相符合音频测试标准音源，即确定了音频信号的类型，从而确定要变换的频率映射函数。
步骤S300、根据音频信号的类型，通过函数库匹配对应的频率映射函数转换得到处理后的音频文件，再通过傅里叶逆变换得到优化的音频文件。
具体地，所述函数库包括第一频率映射函数和第二频率映射函数，所述第一频率映射函数和第二频率映射函数用于通过对音频频率分布范围进行针对性改变，调整音频频率，来改善音频的音质表现。
如图2所示，所述第一频率映射函数和第二频率映射函数用于通过对音频频率分布范围进行针对性改变具体包括：
S10，预先将所述频域上的音频文件中音频的频段划分为若干个区间；例如两个区间或者三个区间不等；
S20，根据音频信号的类型，对于每个不同区间频段的频率，通过选择第一频率映射函数或者第二频率映射函数进行针对性处理。
进一步地，所述第一频率映射函数为：
Figure PCTCN2018076101-appb-000004
其中f为频域上的音频文件中音频的频率，f'为处理后的音频文件中音频的频率。
如图3所示，所述第一频率映射函数对频域上的音频文件中音频的频率转换处理为(即所述第一频率映射函数公式的具体含义)：
S11，将频域上的音频文件中小于等于330Hz的频率转换为330Hz，用于将音频的低音部分擦除；
S12，将频域上的音频文件中大于330Hz但小于1000Hz的频率保持不变；
S13，将频域上的音频文件中大于等于1000Hz的频率转换为1000Hz，用于将音频的高音部分擦除。
具体地，本发明将频段的调整点划为：330Hz，1000Hz，即小于330Hz划为低频，大于330Hz小于1000Hz为中频，大于1000Hz为高频。所述第一频率映射函数将原来所有小于等于330Hz的频率转换为330Hz，这样就把音频的低音部分擦除掉；原大于330Hz但小于1000Hz的频率不变，大于等于1000Hz的频率调整为1000Hz，即高频信息消失。该函数保留中频部分，压缩低频和高频分布范围，主要应用于语言节目等音源，目的是减少低频和高频分量，可以使背景噪音减小，声音清晰纯正。
进一步地，所述第二频率映射函数为：
Figure PCTCN2018076101-appb-000005
其中f为频域上的音频文件中音频的频率，f'为处理后的音频文件中音频的频率，k为扩展系数。
如图4所示，所述第二频率映射函数对频域上的音频文件中音频的频率转换处理为(即所述第二频率映射函数公式的具体含义)：
S21，将频域上的音频文件中小于等于330Hz的频率转换为330Hz，用于将音频的低音部分擦除；
S22，将频域上的音频文件中大于330Hz的频率进行k倍扩大。
具体地，本发明所述第二频率映射函数将小于等于330Hz的音频频率擦除，将大于330Hz的音频频率进行扩大，扩大了大于330Hz的频率范围，即扩展中音和高音部分，压缩低音，主要用来处理金属音乐演奏，强调中音高音来表现金属打击的声音。当然，k还可以为压缩系数(即k﹤1，此时对音频中的一些频率进行压缩)。
本发明中的所述函数库优先由第一频率映射函数和第二频率映射函数组成，当然还可以包括其他频率映射函数，函数库中的频率映射函数主要通过对音频频率分布范围进行针对性改变，来改善音频的音质表现。函数库的函数用来实现音频文件(F[y(t)])到音频文件(F'[y(t)])的变换，这个变换可以是线性变换，非线性变换(对数变换、指数变换)或者分段线性变换(如第一频率映射函数和第二频率映射函数)、分段非线性变换。函数的表达式可以根据频段(高频、中频、低频)划分点的不同分段表示，以达到对音频信号的细化或者简略处理。根据专业调音师的要求，不断增加针对不同音质改善的表达式，这些函数构成了专业化的面向音频处理的函数库。根据应用要求的不同，选择不同的变换函数，这些函数将有选择地对某一频率范围进行扩展或压缩。
本发明的技术方案区别于传统的滤波改善音质，本发明将频率与频率进行映射，通过频率映射函数压缩或扩展相关频率，针对性地修饰音色，达到改善音质的效果，优化听觉效果；通过与标准音源的对比匹配，寻找相近的音源类型，进而确定变换的映射函数，这样能自动调音，并达到专业调音的效果。
本发明还提供了一种移动终端，如图5所示，所述移动终端包括：处理器 (processor)10、存储器(memory)20、通信接口(Communications Interface)30和总线40；
其中，所述处理器10、存储器20、通信接口30通过所述总线40完成相互间的通信；
所述通信接口30用于所述移动终端的通信设备之间的信息传输；
所述处理器10用于调用所述存储器20中的计算机程序，以执行上述各方法实施例所提供的方法，例如包括：预先将时域上的原始音频文件通过傅里叶变换转换为频域上的音频文件；提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，确定音频信号的类型；根据音频信号的类型，通过函数库匹配对应的频率映射函数转换得到处理后的音频文件，再通过傅里叶逆变换得到优化的音频文件。
本发明还提供一种存储装置，其中，所述存储装置存储有计算机程序，所述计算机程序能够被执行以用于实现所述基于算法的音频优化方法。
综上所述，本发明提供了一种基于算法的音频优化方法、智能终端及存储装置，所述方法包括：预先将时域上的原始音频文件通过傅里叶变换转换为频域上的音频文件；提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，确定音频信号的类型；根据音频信号的类型，通过函数库匹配对应的频率映射函数转换得到处理后的音频文件，再通过傅里叶逆变换得到优化的音频文件。本发明通过原始音频与标准音源的对比匹配，寻找相近的音源类型，进而确定变换的频率映射函数，将频率进行映射，压缩或扩展相关频率，自动进行调音，达到改善音质的效果；补偿声音表现在人听觉上的缺陷，从而达到更好的听觉效果。
当然，本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关硬件(如处理器，控制器等)来完成，所述的程序可存储于一计算机可读取的存储介质中，该程序在执行时可包括如上述各方法实施例的流程。其中所述的存储介质可为存储器、磁碟、光盘等。
应当理解的是，本发明的应用不限于上述的举例，对本领域普通技术人员来说，可以根据上述说明加以改进或变换，所有这些改进和变换都应属于本发明所附权利要求的保护范围。

Claims (15)

  1. 一种基于算法的音频优化方法，其特征在于，所述方法包括：
    预先将时域上的原始音频文件通过傅里叶变换转换为频域上的音频文件；
    提取音频信号的频率范围和频幅信息，与已存在的多种不同类型的音频测试标准音源的频率范围和频幅信息匹配，确定音频信号的类型；
    根据音频信号的类型，通过函数库匹配对应的频率映射函数转换得到处理后的音频文件，再通过傅里叶逆变换得到优化的音频文件。
  2. 根据权利要求1所述的基于算法的音频优化方法，其特征在于，所述函数库包括第一频率映射函数和第二频率映射函数，所述第一频率映射函数和第二频率映射函数用于通过对音频频率分布范围进行针对性改变，调整音频频率。
  3. 根据权利要求2所述的基于算法的音频优化方法，其特征在于，所述第一频率映射函数和第二频率映射函数用于通过对音频频率分布范围进行针对性改变具体包括：
    预先将所述频域上的音频文件中音频的频段划分为若干个区间；
    根据音频信号的类型，对于每个不同区间频段的频率，通过选择第一频率映射函数或者第二频率映射函数进行针对性处理。
  4. 根据权利要求3所述的基于算法的音频优化方法，其特征在于，所述第一频率映射函数为：
    Figure PCTCN2018076101-appb-100001
    其中f为频域上的音频文件中音频的频率，f'为处理后的音频文件中音频的频率。
  5. 根据权利要求4所述的基于算法的音频优化方法，其特征在于，所述第一频率映射函数对频域上的音频文件中音频的频率转换处理为：
    将频域上的音频文件中小于等于330Hz的频率转换为330Hz,用于将音频的低音部分擦除；
    将频域上的音频文件中大于330Hz但小于1000Hz的频率保持不变；
    将频域上的音频文件中大于等于1000Hz的频率转换为1000Hz，用于将音频的高音部分擦除。
  6. 根据权利要求3所述的基于算法的音频优化方法，其特征在于，所述第二频率映射函数为：
    Figure PCTCN2018076101-appb-100002
    其中f为频域上的音频文件中音频的频率，f'为处理后的音频文件中音频的频率，k为扩展系数。
  7. 根据权利要求6所述的基于算法的音频优化方法，其特征在于，所述第二频率映射函数对频域上的音频文件中音频的频率转换处理为：
    将频域上的音频文件中小于等于330Hz的频率转换为330Hz,用于将音频的低音部分擦除；
    将频域上的音频文件中大于330Hz的频率进行k倍扩大。
  8. 根据权利要求3所述的基于算法的音频优化方法，其特征在于，所述第一频率映射函数用于保留音频的中频部分，压缩低频和高频分布范围，应用于减小背景噪音和保持声音清晰纯正的语言节目；
    所述第二频率映射函数用于扩展音频的中音和高音部分，压缩低音部分，应用于强调中音高音来表现金属打击声音的金属音乐演奏。
  9. 根据权利要求5所述的基于算法的音频优化方法，其特征在于，将频段的调整点划为：330Hz和1000Hz，小于330Hz划为低频，大于330Hz小于1000Hz为中频，大于1000Hz为高频。
  10. 根据权利要求9所述的基于算法的音频优化方法，其特征在于，所述第一频率映射函数将原来所有小于等于330Hz的频率转换为330Hz,将音频的低音部分擦除掉；原大于330Hz但小于1000Hz的频率不变，大于等于1000Hz的频率调整为1000Hz，高频信息消失。
  11. 根据权利要求9所述的基于算法的音频优化方法，其特征在于，所述第二频率映射函数将小于等于330Hz的音频频率擦除，将大于330Hz的音频频率进行扩大，扩大了大于330Hz的频率范围，扩展中音和高音部分，压缩低音。
  12. 根据权利要求1所述的基于算法的音频优化方法，其特征在于，函数库中的频率映射函数通过对音频频率分布范围进行针对性改变，来改善音频的音质表现，函数库的函数用来实现音频文件(F[y(t)])到音频文件(F'[y(t)])的变 换，变换为线性变换，非线性变换、分段线性变换或者分段非线性变换；函数的表达式根据频段划分点的不同分段表示。
  13. 根据权利要求12所述的基于算法的音频优化方法，其特征在于，将频率与频率进行映射，通过频率映射函数压缩或扩展相关频率。
  14. 一种智能终端，其特征在于，包括：处理器、与处理器通信连接的存储器，所述存储器存储有计算机程序，所述计算机程序用于被执行时实现如权利要求1-13任一项所述的方法；所述处理器用于调用所述存储器中的计算机程序，以实现如权利要求1-13任一项所述的方法。
  15. 一种存储装置，其特征在于，所述存储装置存储有计算机程序，所述计算机程序能够被执行用于实现如权利要求1至13任一项所述的基于算法的音频优化方法。
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