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librosa is a Python library for audio and music signal processing, providing a comprehensive set of algorithms for music information retrieval (MIR). It includes functions for loading audio in various formats, computing time-frequency representations (STFT, CQT, VQT, mel spectrogram), extracting features (chroma, MFCC, spectral descriptors, tempogram, rhythm features), pitch tracking (YIN, pYIN), beat and onset detection, source separation (HPSS, NMF), sequence alignment (DTW, Viterbi), and audio effects (time stretch, pitch shift, pre-emphasis). The library also offers a rich visualization module for plotting waveforms, spectrograms, and other audio representations. The architecture is modular, organized into subpackages: `librosa.core` for core DSP and I/O, `librosa.feature` for feature extraction, `librosa.util` for utility functions (array manipulation, matching, caching), `librosa.filters` for filter bank construction, `librosa.display` for visualization, and domain-specific modules like `librosa.beat`, `librosa.onset`, `librosa.decompose`, `librosa.effects`, `librosa.segment`, and `librosa.sequence`. Submodules are lazily loaded to reduce import time. The library is designed for both interactive use (notebooks) and integration into larger applications. Key entry points include `librosa.load` for audio I/O, `librosa.stft`/`librosa.cqt` for time-frequency transforms, `librosa.feature.*` for feature extraction, `librosa.beat.beat_track` for beat tracking, `librosa.onset.onset_detect` for onset detection, `librosa.display.specshow`/`librosa.display.waveshow` for visualization, and `librosa.effects.*` for audio effects. Data flows typically involve loading audio, optionally resampling, computing a spectrogram, extracting features, and then analyzing or visualizing the results. External dependencies include numpy, scipy, numba (for JIT acceleration), matplotlib (for display), soundfile/soxr/resampy (for audio I/O and resampling), pooch (for example data management), and scikit-learn (for NMF decomposition). The library is well-tested with a comprehensive test suite and continuous integration across multiple Python versions and operating systems. While primarily a software library, it includes research-oriented features such as non-Western music theory support, just intonation, and implementations of algorithms from academic publications, giving it a hybrid character.

Harmonica is a Python library for forward modeling, processing, and inversion of gravity and magnetic data, part of the Fatiando a Terra project. It provides high-performance, Numba-accelerated kernels for computing gravitational and magnetic fields of various geometric bodies (prisms, tesseroids, point masses, ellipsoids, dipoles) and supports equivalent source interpolation, frequency-domain grid transformations (upward continuation, reduction to pole, derivatives), spherical harmonic models (IGRF), and Euler deconvolution. The library is designed for geoscientists and educators, integrating tightly with the scientific Python ecosystem (xarray, scikit-learn, pygmt, verde). It features a scikit-learn-like API for its gridder classes, extensive documentation and testing, and is actively maintained as part of the Fatiando project. While its core functionality is domain-specific, the code exhibits strong software engineering practices: modular architecture, Numba JIT compilation, consistent interfaces, and comprehensive CI/CD.

micropython-ulab is a C library that provides a subset of NumPy and SciPy functionality for MicroPython and CircuitPython on microcontrollers. It implements a multi-dimensional ndarray type with broadcasting, universal functions, linear algebra (Cholesky, eig, inv, QR), FFT, signal processing (sosfilt), numerical integration, optimization, random number generation, and file I/O. The library is designed for performance and memory frugality, using compile-time feature flags (ulab.h) to trim flash usage. Key entry points include ulab.numpy (array creation, math, stats, linalg, fft, io), ulab.scipy (integrate, linalg, optimize, signal, special), and utils (buffer conversion, spectrogram). The architecture is modular with separate C modules for each functional area, a reusable user module for custom extensions, and extensive test coverage for 1D–4D arrays and complex numbers. The library is actively maintained with frequent releases, but some SciPy functions remain incomplete and the codebase has repetitive dtype-dispatch macros that increase maintenance burden.