AudioFlux AudioFlux is a comprehensive library designed for audio and music analysis, offering a wide range of feature extraction capabilities. It serves as a valuable tool for tasks such as audio classification, speech enhancement, music information retrieval (MIR), automatic speech recognition (ASR), and more. By providing systematic and multi-dimensional feature extraction, AudioFlux facilitates both traditional machine learning and deep learning applications in the audio domain. Key Features and Functionality: - Feature Extraction: Supports extraction of various audio features, including mel spectrograms and MFCCs, essential for audio classification and speech enhancement tasks. - Modular Design: Built with a data flow architecture that decouples algorithm modules, enabling efficient and rapid feature extraction from large datasets. - Transform Methods: Offers multiple transform methods such as Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Non-Stationary Gabor Transform (NSGT) for diverse audio analysis needs. - Music Information Retrieval (MIR): Includes algorithms for pitch estimation (e.g., YIN, STFT), onset detection (e.g., flux, novelty), and harmonic-percussive source separation (HPSS) using median filtering and NMF algorithms. - Cross-Disciplinary Applications: Applicable in various fields beyond audio analysis, including deep learning, pattern recognition, signal processing, bioinformatics, statistics, and finance. Primary Value and User Solutions: AudioFlux addresses the need for a versatile and efficient tool in audio analysis by offering a comprehensive suite of feature extraction and transformation methods. Its modular design ensures scalability and adaptability, making it suitable for both research and practical applications across multiple disciplines. By streamlining the process of audio feature extraction and analysis, AudioFlux empowers users to develop and implement advanced audio processing solutions effectively.