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 torchaudio Python 0.11.0 Data manipulation...
python_speech_features较为简单,适用于初学者。 </details> AI检测代码解析 # 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.trans...
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。 LibraryLanguageVersionAbout audioFlux C/Python 0.1.5 A library for audio and music analysis, feature extraction torchaudio Python 0.11.0 Data manipulation and ...
librosa: 纯python开发,主要基于numpy和scipy,numpy底层使用OpenBLAS; Essentia: 基于C++开发和python包装,底层使用Eigen,FFTW; 针对音频领域最常见的mel特征,涉及到性能主要卡点有FFT计算,矩阵计算,多线程并行处理这三部分,其它次要卡点有算法业务实现,python包装等。 针对FFT计算,librosa使用scipy的fftpack实现FFT计算加速...
The audio features were obtained using functions from the Python language package libROSA version 0.10. The calculation of the features was performed by dividing the signal into frames and then applying the functions from the libROSA package. The results obtained from each frame were used to calculat...
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 ...
All feature extraction was realized with librosa [19, 20]. When performing Bayesian optimization, it is helpful to "seed" the optimization with objective values for many randomly-chosen parameter settings to ensure that the optimization thoroughly ex- plores the possible parameter space. We computed...
To validate the audio data, we employed the Librosa library47to preprocess the audio data into a usable format. Subsequently, we used a standard feature extraction method known as Mel-Frequency Cepstral Coefficients (MFCCs)48. MFCCs have been widely validated on extensive audio datasets and are a...
The func- tion librosa.piptrack(pitches, magnitudes) returns two 2D arrays with frequency and time axes. The "pitches" array gives the interpolated frequency estimate of a particular harmonic, and the corresponding value in the "magni- tudes" array gives the energy of the peak. Figure ...