Comparison of four classifiers for speech-music discrimination: a first case study for costa rican radio broadcasting

Abstract

During the past decades, a vast amount of audio data has be- come available in most languages and regions of the world. The efficient organization and manipulation of this data are important for tasks such as data classification, searching for information, diarization among many others, but also can be relevant for building corpora for training models for automatic speech recognition or building speech synthesis systems. Several of those tasks require extensive testing and data for specific languages and accents, especially when the development of communication systems with machines is a goal. In this work, we explore the application of several classifiers for the task of discriminating speech and music in Costa Rican radio broadcast. This discrimination is a first task in the exploration of a large corpus, to determine whether or not the available information is useful for particular research areas. The main contribution of this exploratory work is the general procedure and selection of algorithms for the Costa Rican radio corpus, which can lead to the extensive use of this source of data in many own applications and systems.