TUT database for acoustic scene classification and sound event detection

Annamaria Mesaros, Toni Heittola, Tuomas Virtanen

2016 · 525 citations · 20 references

DOIFull text

Open access

Concepts

TL;DR

The paper introduces the TUT Acoustic Scenes 2016 database, detailing its binaural recordings from 15 environments, the annotation process, and baseline performance of acoustic scene classification and sound event detection systems. The database includes a subset, TUT Sound Events 2016, with manually annotated residential and home sound events, and the authors employ mel‑frequency cepstral coefficients and Gaussian mixture models with a recommended cross‑validation setup to evaluate supervised classification and detection. The publicly released database aims to support algorithm development and provide a common benchmark for comparing different techniques.

Abstract

We introduce TUT Acoustic Scenes 2016 database for environmental sound research, consisting of binaural recordings from 15 different acoustic environments. A subset of this database, called TUT Sound Events 2016, contains annotations for individual sound events, specifically created for sound event detection. TUT Sound Events 2016 consists of residential area and home environments, and is manually annotated to mark onset, offset and label of sound events. In this paper we present the recording and annotation procedure, the database content, a recommended cross-validation setup and performance of supervised acoustic scene classification system and event detection baseline system using mel frequency cepstral coefficients and Gaussian mixture models. The database is publicly released to provide support for algorithm development and common ground for comparison of different techniques.

References

20