Preprocessing audio data involves a different set of steps, here’s a guide on how you can preprocess audio data:

Preprocessing Libraries:

Python libraries commonly used for audio preprocessing are Librosa, pyaudio, pydub, NumPy, SciPy or scikit-learn. They offer functions and tools for various speech pre-processing tasks.

  1. Resampling (down-sampling or up-sampling):

Resampling is the process of modifying the sampling frequency of an audio signal. Resampling is often performed to adapt the sampling frequency of the audio signal to the requirements of a particular application or system. librosa.resample()

  1. Pre-emphasizing:

Pre-emphasis is a filtering technique applied to speech signals to emphasize high-frequency components. It is commonly used to improve the signal-to-noise ratio and performance of speech processing algorithms. scipy.signal.lfilter()

  1. Framing:

Signal framing, also known as speech segmentation, is the process of partitioning continuous speech signals into fixed-length segments in order to solve the problem of non-stationarity. pydub.AudioSegment()

  1. Windowing:

After framing the speech signal, the next step is usually to apply a windowing function to the frames. The windowing function is used to reduce the effects of leakage occurring during Fast Fourier Transform (FFT) of the data, caused by discontinuities at the edges of the signals. The Hamming window is the most commonly used. librosa.filters.get_window()

  1. Voice activity detection:

or endpoint detection consists of three parts; voiced speech, unvoiced speech, and silence. Voiced speech is generated with the vibration of vocal folds that creates periodic excitation to the vocal tract during the pronunciation of phonemes which are perceptually distinct units of sound that distinguish one word from another. Unvoiced speech is the result of air passing through a constriction in the vocal tract, producing transient and turbulent noises that are aperiodic excitations of the vocal tract. Most widely used methods for voice activity detection are zero crossing rate, short time energy, and auto-correlation method. librosa.feature.zero_crossing_rate(), librosa.feature.rms(), numpy.correlate()

  1. Normalization:

Normalization is an important step in reducing speaker and recording variability without losing the discriminative power of the features. Feature normalization increases the generalizability of features. The most widely used normalization method is z-normalization (z-score). sklearn.preprocessing.StandardScaler()

  1. Noise reduction:

noise in the environment is captured with the speech signal, affecting the recognition rate. Minimum mean square error (MMSE) and logarithmic amplitude MMSE (LogMMSE) estimators are the most widely used methods for noise reduction. logmmse.logmmse_from_file()