CHALLENGES AND ISSUES OF SOUND ARCHIVES FOR ENVIRONMENTAL SOUND CLASSIFICATION

Combining frame and segment based models for environmental sound classification

The paper considers the task of recognizing environmental sounds, which plays a critical role in human’s perception of an auditory context in audiovisual materials. A variety of features have been proposed for audio recognition, either frame-based or segmental. Here, we propose a two-stage framework to combine modeling in these two levels. First, the Gaussian Mixture Models(GMMs) are built base...

International Association of Sound and Audiovisual Archives

Natural sound archives: past, present and future.

Recordings of wild animals were first made in the Palearctic in 1900, in the Nearctic in 1929, in Antarctica in 1934, in Asia in 1937, and in the Neotropics in the 1940s. However, systematic collecting did not begin until the 1950s. Collections of animal sound recordings serve many uses in education, entertainment, science and nature conservation. In recent years, technological developments hav...

Masked Conditional Neural Networks for Environmental Sound Classification

The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network’s links using a binary mask. Additionally, the masking automates the exploration of different...

A multidomain approach for automatic home environmental sound classification

This article presents a multidomain approach which addresses the problem of automatic home environmental sound recognition. The proposed system will be part of a human activity monitoring system which will be based on heterogeneous sensors. This work concerns the audio classification component and its primary role is to detect anomalous sound events. We compare the discriminative capabilities o...