Audio Classification
Overview
Audio classification studies how to assign sounds to labels, from spoken words and music genres to bird calls, machine faults, and respiratory conditions. Systems commonly use spectrograms, mel-frequency cepstral coefficients, or raw waveforms with convolutional networks, transformers, and pretrained or self-supervised audio encoders; limited-label settings also use transfer learning, few-shot methods, and audio-language models. Benchmarks such as AudioSet, ESC-50, UrbanSound8K, and ICBHI test different tasks, but strong benchmark scores may not carry over across recording devices, locations, speakers, or species. A central challenge is building reliable models from scarce or weak labels while avoiding data leakage and shortcuts, and keeping models efficient enough for clinical and on-device use.
Latest papers
147 papers18 papers in the last four weeks, up 6% on the four weeks before. 0.1% of all new papers.