Audio Summarization with Audio Features and Probability Distribution Divergence

Features for Audio Classification

Four audio feature sets are evaluated in their ability to differentiate five audio classes: popular music, classical music, speech, noise and crowd noise. The feature sets include low-level signal properties, mel-frequency spectral coefficients, and two new sets based on perceptual models of hearing. The temporal behavior of the features is analyzed and parameterized and these parameters are in...

Exploring Features for Audio

Latent Topic Modeling for Audio Corpus Summarization

This work presents techniques for automatically summarizing the topical content of an audio corpus. Probabilistic latent semantic analysis (PLSA) is used to learn a set of latent topics in an unsupervised fashion. These latent topics are ranked by their relative importance in the corpus and a summary of each topic is generated from signature words that aptly describe the content of that topic. ...

Evaluation of Features for Audio-to-Audio Alignment

Audio-to-audio alignment is the task of synchronizing two audio sequences with similar musical content in time. We investigated a large set of audio features for this task. The features were chosen to represent four different content-dependent similarity categories: the envelope, the timbre, note-onsets and the pitch. The features were subjected to two processing stages. First, a feature subset...

Ontological Representation of Audio Features

Feature extraction algorithms in Music Informatics aim at deriving statistical and semantic information directly from audio signals. These may be ranging from energies in several frequency bands to musical information such as key, chords or rhythm. There is an increasing diversity and complexity of features and algorithms in this domain and applications call for a common structured representati...