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This paper presents a novel approach to visualiz- ing the time structure of musical waveforms. The acoustic similarity between any two instants of an audio recording is displayed in a static 2D repre- sentation, which makes structural and rhythmic characteristics visible. Unlike practically all prior work, this method characterizes self-similarity rather than specific audio attributes such as pitch or spectral features. Examples are presented for classical and popular music.

Similarity matrices have become an important tool in music audio analysis. However, the quadratic time and space complexity as well as the intricacy of extracting the desired structural information from these matrices are often prohibitive with regard to real-world appli- cations. In this paper, we describe an approach for enhancing the structural properties of similarity matrices based on two concepts: first, we introduce a new class of robust and scalable audio features which absorb local temporal variations. As a second contribution, we then incorporate contextual information into the local similarity measure. The resulting enhancement leads to significant reduction in matrix size and also eases the structure extraction step. As an exam- ple, we sketch the application of our techniques to the problems of audio summarization and audio synchronization, obtaining effective and computationally feasible algorithms.