Python's pyAudioAnalysis: Audio Feature Extraction Analysis Document Example Detailed Explanation

PyAudioAnalysis is an open source Python library for feature extraction and analysis from audio files. It provides a series of audio processing functions that can help developers implement various tasks such as audio classification, emotion recognition, and speech analysis. In this article, we will detail how to use PyAudioAnalysis for audio feature extraction and analysis.

1. Audio feature extraction
   PyAudioAnalysis provides a variety of methods for extracting audio features. These features can be used to describe the basic properties and characteristics of audio, including time-domain features, frequency-domain features, and spectrogram features.

   (1) Extract time domain features:

   from pyAudioAnalysis import audioBasicIO
   from pyAudioAnalysis import audioFeatureExtraction
   
   audio_path = 'audio.wav'
   
   # 读取音频文件
   [audio_signal, fs] = audioBasicIO.read_audio_file(audio_path)
   
   # 提取时域特征
   [mt_features, st_features] = audioFeatureExtraction.stFeatureExtraction(audio_signal, fs, 0.050 * fs, 0.025 * fs)
   
   在上述代码中，首先使用 `audioBasicIO.read_audio_file` 函数读取音频文件，返回音频信号和采样率。然后，使用 `audioFeatureExtraction.stFeatureExtraction` 函数提取短时特征和中时特征。其中，`0.050 * fs` 表示分析窗口为50毫秒，`0.025 * fs` 表示窗口之间的间隔为25毫秒。
   

   (2) Extract frequency domain features:

   from pyAudioAnalysis import audioBasicIO
   from pyAudioAnalysis import audioFeatureExtraction
   
   audio_path = 'audio.wav'
   
   # 读取音频文件
   [audio_signal, fs] = audioBasicIO.read_audio_file(audio_path)
   
   # 提取频域特征
   [fbank, freq_bands] = audioFeatureExtraction.stFeatureExtraction(audio_signal, fs, 0.050 * fs, 0.025 * fs)
   
   
   上述代码中的 `audioBasicIO.read_audio_file` 和 `audioFeatureExtraction.stFeatureExtraction` 函数用法与前面的相同，只是提取的是频域特征。
   

   (3) Extract spectral features:

   from pyAudioAnalysis import audioBasicIO
   from pyAudioAnalysis import audioFeatureExtraction
   
   audio_path = 'audio.wav'
   
   # 读取音频文件
   [audio_signal, fs] = audioBasicIO.read_audio_file(audio_path)
   
   # 提取谱图特征
   spec_features = audioFeatureExtraction.stFeatureExtraction(audio_signal, fs, 0.050 * fs, 0.025 * fs)
   
   
   在上述代码中，通过 `audioBasicIO.read_audio_file` 函数读取音频文件，然后使用 `audioFeatureExtraction.stFeatureExtraction` 函数提取谱图特征。
   

2. Audio feature analysis
   After extracting audio features, we can use PyAudioAnalysis for further analysis, such as classification or emotion recognition.

   (1) Audio Category:

   from pyAudioAnalysis import audioTrainTest as aT
   
   model_path = 'svm_model'
   audio_path = 'audio.wav'
   
   # 音频分类
   result, _ = aT.file_classification(audio_path, model_path, 'svm')
   
   
   在上述代码中，`audioTrainTest.file_classification` 函数用于对音频进行分类，需要指定分类模型路径、音频路径和分类器类型（这里选择了支持向量机svm）。
   

   (2) Emotion recognition:

   from pyAudioAnalysis import audioSegmentation as aS
   
   audio_path = 'audio.wav'
   
   # 情感识别
   [emotion, prob] = aS.emotion_extraction(audio_path)
   
   
   上述代码中，`audioSegmentation.emotion_extraction` 函数用于从音频中提取情感信息。
   

The above is an example of the basic usage of PyAudioAnalysis's audio feature extraction and analysis. Next, we will continue to introduce other functions and usage examples of PyAudioAnalysis.

1. Other functions
   PyAudioAnalysis also provides many other useful functions, such as audio cutting, speech recognition and pitch estimation, etc.

   (1) Audio cutting:

   from pyAudioAnalysis import audioSegmentation as aS
   
   audio_path = 'audio.wav'
   
   # 音频切割
   segments = aS.silence_removal(audio_path)
   
   
   上述代码中，`audioSegmentation.silence_removal` 函数用于从音频中删除静音部分，并返回非静音片段的起始和终止时间。
   

   (2) Speech recognition:

   ```
   from pyAudioAnalysis import audioSegmentation as aS
   from pyAudioAnalysis import audioTranscription
   
   audio_path = 'audio.wav'
   
   # 语音识别
   transcription = audioTranscription.transcribe_audio(audio_path, 'en')
   ```
   
   在上述代码中，首先使用 `audioSegmentation` 模块的函数剔除音频中的静音部分，然后使用 `audioTranscription.transcribe_audio` 函数对不含静音的音频进行文字转录（这里以英文为例）。
   

   (3) Fundamental frequency estimation:

   from pyAudioAnalysis import audioBasicIO
   from pyAudioAnalysis import audioFeatureExtraction
   
   audio_path = 'audio.wav'
   
   # 读取音频文件
   [audio_signal, fs] = audioBasicIO.read_audio_file(audio_path)
   
   # 基频估计
   pitch = audioFeatureExtraction.pitch_contour(audio_signal, fs)
   
   
   在上述代码中，通过 `audioBasicIO.read_audio_file` 函数读取音频文件，然后使用 `audioFeatureExtraction.pitch_contour` 函数进行基频估计，返回基频轮廓。

2. Conclusion
   In this article, we have detailed an example of how to use PyAudioAnalysis for audio feature extraction and analysis. By extracting time domain features, frequency domain features and spectrogram features, we can obtain the basic properties and characteristics of audio. At the same time, we also introduce sample codes for functions such as audio classification, emotion recognition, audio cutting, speech recognition, and pitch estimation.

   In summary, PyAudioAnalysis is a powerful and flexible tool for a variety of tasks in audio processing and analysis. Developers can flexibly use these functions according to specific needs to realize various audio processing and analysis applications. Understanding and mastering how to use PyAudioAnalysis will be very helpful for audio-related project development.