Speech/Music Discrimination using Spectral Peak Feature for Speaker Indexing

We present a new speech/music discrimination method based on spectral peak feature and spectral peak´s duration threshold. The focus is feature extraction that reflects the spectral peak´s duration characteristic. Also, we consider fast discrimination and high performance. We extract the spectral peak feature from audio spectrum´s each peak track and normalize the feature by length of segment. The extracted spectral peak´s duration feature can be easily discriminated the speech and music using the duration threshold. We evaluate our method on speech (Korean, English, Chinese and Japanese) and various kinds of pop-music (ballad, rock etc.) for 26,773 seconds of audio data. The average accuracy is 96.21% for speech and 89.49% for music. It was found from the experimental result that our feature vector is suitable for speech/music discrimination and it is computational efficient