AudioFlux is a Python library that provides deep learning tools for audio and music analysis and feature extraction. It supports various time-frequency analysis transformation methods, which are techniques for analyzing audio signals in both the time and frequency domains. Some examples of these transformation methods include the short-time Fourier transform (STFT), the constant-Q transform (CQT), and the wavelet transform.
In addition to the time-frequency analysis transformations, AudioFlux also supports hundreds of corresponding time-domain and frequency-domain feature combinations. These features can be used to represent various characteristics of the audio signal, such as its spectral content, its temporal dynamics, and its rhythmic patterns. These features can be extracted from the audio signal and used as input to deep learning networks for classification, separation, music information retrieval (MIR) tasks, and automatic speech recognition (ASR).
For example, in music classification, AudioFlux could extract a set of features from a piece of music, such as its spectral centroid, mel-frequency cepstral coefficients (MFCCs), and its zero-crossing rate. These features could then be used as input to a deep learning network trained to classify the music into different genres, such as rock, jazz, or hip-hop. AudioFlux provides a comprehensive set of tools for analyzing and processing audio signals. This is an essential asset for professionals and scholars studying and applying methods to analyze audio and music.