python_speech_features较为简单,适用于初学者。 </details> # librosa 提取 MFCC 示例importlibrosa y,sr=librosa.load('audio.wav')mfccs=librosa.feature.mfcc(y=y,sr=sr)# torchaudio 提取 MFCC 示例importtorchaudio waveform,sample_rate=torchaudio.load('audio.wav')mfccs=torchaudio.transforms.MFCC()(w...
在深度学习音频领域,mel频谱是最常用的音频特征。在本文中,我们将对四个常用的音频处理库——audioflux、torchaudio、librosa和essentia——进行性能测试,以评估它们在计算Mel频谱时的效率。 Library Language Version About audioFlux C/Python 0.1.5 A library for audio and music analysis, feature extraction ...
包括后面要提到的离散傅里叶变换,目前常见的python工具包例如librosa,torchaudio ,tf.audio等等,也都是统一都是使用复指数形式下的幅度谱和相位谱的。 傅里叶变换 我们前面提到过傅里叶级数针对的是时域周期且连续的信号,但是生活中我们能够接触到的大部分声音信号,例如人说话的声音,去观测它的波形,基本上不太可能...
Sonopy SpeechPy python_speech_features librosaCreditsThanks to SpeechPy for providing an example of the concrete calculations for MFCCs. Much of the calculations in this library take influence from it.AboutA simple audio feature extraction library ...
git clone git@github.com:SuperKogito/spafe.git cd spafe python setup.py install Why use Spafe? Unlike most existing audio feature extraction libraries (python_speech_features,SpeechPy,surfboardandBob), Spafe provides more options for spectral features extraction algorithms, notably: ...
音频信号处理在各种应用中都发挥着重要的作用,如语音识别、音乐信息检索、语音合成等。其中,Mel频谱是一种常用的频域特征表示方法,用于描述人类听觉系统对...
在深度学习音频领域,mel频谱是最常用的音频特征。在本文中,我们将对四个常用的音频处理库——audioflux、torchaudio、librosa和essentia——进行性能测试,以评估它们在计算Mel频谱时的效率。 audioFlux:基于C开发和python包装,底层针对不同平台有不同的桥接处理,支持OpenBLAS,MKL等 ...
针对FFT计算,librosa使用scipy的fftpack实现FFT计算加速,比FFTW3,MKL,Accelerate要慢一些; 针对矩阵计算,MKL比OpenBLAS要快些,OpenBLAS比其Eigen快一些; 针对多线程并行处理,具体各个项目内部是否有支持。 测试脚本 测试多个库,使用以下方式: $ python run_benchmark.py -p audioflux,torchaudio,librosa -r 1000 -er...
Feature extraction is extracting features to use them for analysis. There are a lot of libraries in python for working on audio data analysis like: Librosa Ipython.display.Audio Spacy, etc. Centroid of wave: During any sound emission we may see our complete sound/audio data focused on a ...
Rather than creating a "floating point time series" in this script vialibrosa.load, we rely on ffmpeg to get the PCM data - this is considerably faster for audio files that are over an hour long. Rather than processing the whole file, then comparing via STFT (uses a lot of memory), ...