A Generic Audio Identification System for Radio Broadcast Monitoring Based on Data-Driven Segmentation

In this paper, a generic audio identification system is introduced to identify advertisements and songs in radio broadcast streams using automatically acquired segmental units. A new fingerprinting method based on ALISP data-driven segmentation is presented. A modified BLAST algorithm is also proposed for fast and approximate matching of ALISP sequences. To detect commercials and songs, ALISP transcriptions of references composed of large library of commercials and songs, are compared to the transcriptions of the test radio stream using Levenshtein distance. The system is described and evaluated on broadcast audio streams from 12 French radio stations. For advertisement identification, a mean precision rate of 100% with the corresponding recall value of 98% were achieved. For music identification, a mean precision rate of 100% with the corresponding recall value of 95% were achieved.