Structural Segmentation using Set Accented Tones

An approach which efficiently segments Irish Traditional Music into its constituent structural segments is presented. The complexity of the segmentation process is greatly increased due to melodic variation existent within this music type. In order to deal with these variations, a novel method using ‘set accented tones’ is introduced. The premise is that these tones are less susceptible to variation than all other tones. Thus, the location of the accented tones is estimated and pitch information is extracted at these specific locations. Following this, a vector containing the pitch values is used to extract similar patterns using heuristics specific to Irish Traditional Music. The robustness of the approach is evaluated using a set of commercially available Irish Traditional recordings

Advances in Similarity-Based Audio Compression

Existing lossy audio compression techniques such as MP3, WMA and Ogg Vorbis, for example, demonstrate great success in providing compression ratios which successfully reduce the data size from the original sampled audio. These techniques employ psychoacoustic models and traditional statistical coding techniques to achieve data reduction. However, these methods do not take into account the perceived content of the audio, which is often particularly relevant in musical audio. In this paper, we present our research and development work completed to date, in producing a system for audio analysis, which will consider and exploit the repetitive nature of audio and the similarities which frequently occur in audio recordings. We demonstrate the feasibility and scope of the analysis system and consider the techniques and challenges that are employed to achieve data reduction

Preferences in Musical Rhythms and Implementation of Analytical Results to Generate Rhythms

Rhythm is at the heart of all music. It is the variation of the duration of sound over time. A rhythm has two components: one is the striking of an instrument – called the onset – and the other is silence. Historically, musical forms and works were preferred and became popular by their rhythmic properties. Therefore, to study rhythm is to study the underpinnings of all of music. In this thesis, we explore basic rhythmic preferences in traditional music and, using this as a point of reference, methods are implemented to generate similar types of rhythms. Finally, a software platform to facilitate such an analysis is developed – it is the first of its kind available to our best knowledge as this research field has only recently emerged

Preferences in Musical Rhythms and Implementation of Analytical Results to Generate Rhythms

Rhythm is at the heart of all music. It is the variation of the duration of sound over time. A rhythm has two components: one is the striking of an instrument – called the onset – and the other is silence. Historically, musical forms and works were preferred and became popular by their rhythmic properties. Therefore, to study rhythm is to study the underpinnings of all of music. In this thesis, we explore basic rhythmic preferences in traditional music and, using this as a point of reference, methods are implemented to generate similar types of rhythms. Finally, a software platform to facilitate such an analysis is developed – it is the first of its kind available to our best knowledge as this research field has only recently emerged

Content-based music structure analysis

Ph.DDOCTOR OF PHILOSOPH

Discovering Musical Structure in Audio Recordings

Music is often described in terms of the structure of repeated phrases. For example, many songs have the form AABA, where each letter represents an instance of a phrase. This research aims to construct descriptions or explanations of music in this form, using only audio recordings as input. A system of programs is described that transcribes the melody of a recording, identifies similar segments, clusters these segments to form patterns, and then constructs an explanation of the music in terms of these patterns. Additional work using spectral information rather than melodic transcription is also described. Examples of successful machine "listening" and music analysis are presented