A guide to using the Python audio processing library

Audio processing is an important branch in the multimedia field. In addition to the music industry, it is also an essential skill in artificial intelligence, human-computer interaction and other fields. In Python, the audio processing library is relatively commonly used, and it can help us with audio collection, processing and analysis. This article will introduce some commonly used Python audio processing libraries and how to use them.

1. PyAudio

PyAudio is a Python module that can help us implement functions such as audio collection and playback in Python. It supports multiple operating systems and can be used not only on Windows systems, but also on Linux and Mac OS X. Using PyAudio, we can easily read and write audio files, as well as record and play audio in real time.

The installation of PyAudio is very simple, you only need to install it through the pip command:

pip install pyaudio

The following is a simple example demonstrating how to use PyAudio to read audio files:

import pyaudio
import wave

# 打开 wav 文件
wave_file = wave.open('test.wav', 'rb')

# 初始化 PyAudio
p = pyaudio.PyAudio()

# 打开音频流
stream = p.open(format=p.get_format_from_width(wave_file.getsampwidth()),
                channels=wave_file.getnchannels(),
                rate=wave_file.getframerate(),
                output=True)

# 读取数据并播放
data = wave_file.readframes(1024)
while data != b'':
    stream.write(data)
    data = wave_file.readframes(1024)

# 停止音频流和 PyAudio
stream.stop_stream()
stream.close()
p.terminate()

# 关闭 wav 文件
wave_file.close()

The above code first uses the wave module to open an audio file, and then uses the PyAudio module to open the audio stream, read the data in the audio file, and write it into the audio stream. Finally, close the audio stream and PyAudio when you are done playing the audio.

2. SciPy

SciPy is a Python library for scientific computing. It supports a variety of scientific applications, including signal processing, image processing, optimization, etc. In audio processing, we usually use the signal module in SciPy to perform signal processing operations such as filtering.

The installation of SciPy is also very simple. You only need to use the pip command to install it:

pip install scipy

The following is a simple example demonstrating how to use SciPy to filter audio data:

import scipy.signal as signal
import scipy.io.wavfile as wav

# 读取音频文件
rate, data = wav.read("test.wav")

# 构造滤波器
nyq_rate = rate / 2.0
cutoff_freq = 2000.0
normal_cutoff = cutoff_freq / nyq_rate
b, a = signal.butter(4, normal_cutoff, btype='lowpass')

# 滤波处理
filtered_data = signal.lfilter(b, a, data)

# 写入输出文件
wav.write("filtered_test.wav", rate, filtered_data.astype(data.dtype))

In the above code, the wav module is used to read the original audio data, then a low-pass filter is constructed, and the signal.lfilter function is used to filter the original data. Finally, use the wav module to write the processed audio data to the output file.

3. LibROSA

LibROSA is a Python library for music and audio analysis. It supports multiple audio file formats and provides many functions for processing audio data. Using LibROSA, we can easily perform operations such as audio feature extraction, audio signal processing and analysis. In addition, LibROSA also encapsulates commonly used feature extraction algorithms, such as audio time domain and frequency domain analysis, Mel frequency filter bank, Mel cepstrum, MFCC, etc.

LibROSA installation method:

pip install librosa

The following is a simple example demonstrating how to use LibROSA for audio analysis:

import librosa

# 读取音频文件
y, sr = librosa.load("test.wav")

# 提取音频特征
# STFT
D = librosa.stft(y)

# 梅尔频率滤波器组 (melspectrogram)
S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,
                                    fmax=8000)

# 梅尔倒谱系数 (MFCCs)
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)

# 显示特征提取结果
import matplotlib.pyplot as plt
librosa.display.specshow(librosa.power_to_db(S, ref=np.max),
                            y_axis='mel', fmax=8000,
                            x_axis='time')

plt.colorbar(format='%+2.0f dB')
plt.title('Mel spectrogram')
plt.tight_layout()
plt.show()

In the above code, use the librosa.load function to read audio data, and then use functions such as librosa.stft, librosa.feature.melspectrogram and librosa.feature.mfcc to extract features from the audio, and display the processed audio feature map.

Summary

This article introduces three commonly used Python audio processing libraries, including PyAudio, SciPy and LibROSA, and demonstrates their use. These libraries can easily implement functions such as audio collection, processing, and analysis. We hope to provide some help to readers who are learning audio processing.

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