The ability of traditional packet level Forward Error Correction approaches can limit errors for small sporadic network losses but when dropouts of large portions occur listening quality becomes an issue. Services such as audio-on-demand drastically increase the loads on networks therefore new, robust and highly efficient coding algorithms are necessary. One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes account of the semantics and natural repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented problematic challenges within the field of Music Information Retrieval. We present a system which works at the content level thus rendering it applicable in existing streaming services. Using the MPEG–7 Audio Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables self-similarity to be performed within polyphonic audio.
|Publication status||Published - 1 Mar 2017|
- streaming audio