The bag-of-audio-words approach has been widely used for audio event recognition. In these models, a local feature of an audio signal is matched to a code word according to a learned codebook. The signal is then represented by frequencies of the matched code words on the whole signal. We present in this paper an improved model based on the idea of audio phrases which are sequences of multiple audio words. By using audio phrases, we are able to capture the relationship between the isolated audio words and produce more semantic descriptors. Furthermore, we also propose an efficient approach to learn a compact codebook in a discriminative manner to deal with high-dimensionality of bag-of-audio-phrases representations. Experiments on the Freiburg-106 dataset show that the recognition performance with our proposed bag-of-audio-phrases descriptor outperforms not only the baselines but also the state-of-the-art results on the dataset.
|Title of host publication||2015 23rd European Signal Processing Conference (EUSIPCO)|
|Number of pages||5|
|Publication status||Published - 01.08.2015|
|Event||23rd European Signal Processing Conference - Nice Congress Center Nice, Nice, France|
Duration: 31.08.2015 → 04.09.2015
Conference number: 118897