ALISP-Based Data Compression for Generic Audio Indexing

In this paper we propose a generic framework to index and retrieve audio. In this framework, audio data is transformed into a sequence of symbols using the ALISP tools. In such a way the audio data is represented in a compact way. Then an approximate matching algorithm inspired from the BLAST technique is exploited to retrieve the majority of audio items that could be present in radio stream. The evaluations of the proposed systems are done on a private radio broadcast database provided by YACAST and other publicly available corpora. The experimental results show an excellent performance in audio identification (for advertisement and songs), audio motif discovery (for advertisement and songs), speaker diarization and laughter detection. Moreover, the ALISP-based system has obtained the best results in ETAPE 2011 (Evaluations en Treatment Automatique de la Parole) evaluation campaign for the speaker diarization task.