audioanalyse

Audioanalyse is the systematic examination of audio signals to extract information about their content, structure, or properties. It combines signal processing, acoustics, and statistical learning and covers time-domain, frequency-domain, and time-frequency representations. A typical workflow includes preprocessing, segmentation into short frames with windowing, feature extraction, and subsequent classification, regression, or detection tasks.

Core techniques include spectral analysis through the Fourier transform and short-time Fourier transform, yielding spectrograms that show how frequency content evolves over time. Common features used in practice are Mel-frequency cepstral coefficients (MFCCs), chroma vectors, spectral flux, spectral contrast, pitch estimates, and tempo or beat-related features. More advanced representations involve wavelets and learned embeddings from deep networks.

Audioanalyse supports a wide range of applications. In music information retrieval, it enables genre or mood classification, tempo and key estimation, beat tracking, chord and instrument recognition, and music transcription. In speech processing, it underpins automatic speech recognition, speaker identification and diarization, speaking style and emotion detection, and voice activity detection. Environmental or acoustic scene analysis uses audioanalyse to detect events such as alarms, gunshots, or animal sounds.

Challenges include variability in recording conditions, polyphony, background noise, reverberation, and dataset bias. Evaluation relies on annotated benchmarks and metrics such as accuracy, error rate, F1 score, or mean squared error, depending on the task. Widely used tools include Praat, librosa, and MATLAB-based toolkits.