Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack a... more Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack are summed to a single audio bus. This is done so that the mix engineer can apply signal processing to an entire subgroup, speed up the mix work flow and manipulate a number of audio tracks at once. In this work, we investigate which audio features from a set of 159 can be used to automatically subgroup multitrack audio. We determine a subset of audio features from the original 159 audio features to use for automatic subgrouping, by performing feature selection using a Random Forest classifier on a dataset of 54 individual multitracks. We show that by using agglomerative clustering on 5 test multitracks, the entire set of audio features incorrectly clusters 35.08% of the audio tracks, while the subset of audio features incorrectly clusters only 7.89% of the audio tracks. Furthermore, we also show that using the entire set of audio features, ten incorrect subgroups are created. However, when using the subset of audio features, only five incorrect subgroups are created. This indicates that our reduced set of audio features provides a significant increase in classification accuracy for the creation of subgroups automatically.
Audio feature extraction underpins a massive proportion of audio processing, music information re... more Audio feature extraction underpins a massive proportion of audio processing, music information retrieval, audio effect design and audio synthesis. Design, analysis, synthesis and evaluation often rely on audio features, but there are a large and diverse range of feature extraction tools presented to the community. An evaluation of existing audio feature extraction libraries was undertaken. Ten libraries and toolboxes were evaluated with the Cranfield Model for evaluation of information retrieval systems, reviewing the coverage, effort, presentation and time lag of a system. Comparisons are undertaken of these tools and example use cases are presented as to when toolboxes are most suitable. This paper allows a software engineer or researcher to quickly and easily select a suitable audio feature extraction toolbox.
Subgrouping is an important part of the mix engineering workflow that facilitates the process of ... more Subgrouping is an important part of the mix engineering workflow that facilitates the process of manipulating a number of audio tracks simultaneously. We statistically analyse the subgrouping practices of mix engineers in order to establish the relationship between subgrouping and mix preference. We investigate the number of subgroups (relative and absolute), the type of audio processing and the subgrouping strategy in 72 mixes of nine songs, by 16 mix engineers. We analyse the subgrouping setup for each mix of a particular song and also each mix by a particular mixing engineer. We show that subjective preference for a mix strongly correlates with the number of subgroups, and to a lesser extent which types of audio processing are applied to the subgroups.
Audio feature extraction underpins a massive proportion of audio processing, music information re... more Audio feature extraction underpins a massive proportion of audio processing, music information retrieval, audio effect design and audio synthesis. Design, analysis, synthesis and evaluation often rely on audio features, but there are a large and diverse range of feature extraction tools presented to the community. An evaluation of existing audio feature extraction libraries was undertaken. Ten libraries and toolboxes were evaluated with the Cranfield Model for evaluation of information retrieval systems, reviewing the coverage , effort, presentation and time lag of a system. Comparisons are undertaken of these tools and example use cases are presented as to when toolboxes are most suitable. This paper allows a software engineer or researcher to quickly and easily select a suitable audio feature extraction toolbox.
Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack a... more Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack are summed to a single audio bus. This is done so that the mix engineer can apply signal processing to an entire subgroup, speed up the mix work flow and manipulate a number of audio tracks at once. In this work, we investigate which audio features from a set of 159 can be used to automatically subgroup multitrack audio. We determine a subset of audio features from the original 159 audio features to use for automatic subgrouping, by performing feature selection using a Random Forest classifier on a dataset of 54 individual multitracks. We show that by using agglomerative clustering on 5 test multitracks, the entire set of audio features incorrectly clusters 35.08% of the audio tracks, while the subset of audio features incorrectly clusters only 7.89% of the audio tracks. Furthermore, we also show that using the entire set of audio features, ten incorrect subgroups are created. However, when using the subset of audio features, only five incorrect subgroups are created. This indicates that our reduced set of audio features provides a significant increase in classification accuracy for the creation of subgroups automatically.
I would like to thank Giuseppe Torre for accepting my dissertation proposal and I would also like... more I would like to thank Giuseppe Torre for accepting my dissertation proposal and I would also like to thank my second reader Paddy Healy for his technical support and guidance. I would also like to thank the Music Technology class of 2009-10 for the good times and also a big thank you to all the faculty for there wisdom and inspiration. Also, thank you to all my family and friends for there warmth and support. ii Abstract There has been very little research done with regard to the physical modelling synthesis of the Indian classical instrument the Sitar. This dissertation intends on expanding on what little research has been done on the subject and attempts to model the instrument with modern modelling techniques such as bi-directional digital waveguides, fractional delay filtering and sympathetic vibrations.
Audio feature extraction underpins a massive proportion of audio processing, music information re... more Audio feature extraction underpins a massive proportion of audio processing, music information retrieval, audio effect design and audio synthesis. Design, analysis, synthesis and evaluation often rely on audio features, but there are a large and diverse range of feature extraction tools presented to the community. An evaluation of existing audio feature extraction libraries was undertaken. Ten libraries and toolboxes were evaluated with the Cranfield Model for evaluation of information retrieval systems, reviewing the coverage, effort, presentation and time lag of a system. Comparisons are undertaken of these tools and example use cases are presented as to when toolboxes are most suitable. This paper allows a software engineer or researcher to quickly and easily select a suitable audio feature extraction toolbox.
Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack a... more Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack are summed to a single audio bus. This is done so that the mix engineer can apply signal processing to an entire subgroup, speed up the mix work flow and manipulate a number of audio tracks at once. In this work, we investigate which audio features from a set of 159 can be used to automatically subgroup multitrack audio. We determine a subset of audio features from the original 159 audio features to use for automatic subgrouping, by performing feature selection using a Random Forest classifier on a dataset of 54 individual multitracks. We show that by using agglomerative clustering on 5 test multitracks, the entire set of audio features incorrectly clusters 35.08% of the audio tracks, while the subset of audio features incorrectly clusters only 7.89% of the audio tracks. Furthermore, we also show that using the entire set of audio features, ten incorrect subgroups are created. However, when using the subset of audio features, only five incorrect subgroups are created. This indicates that our reduced set of audio features provides a significant increase in classification accuracy for the creation of subgroups automatically.
Subgrouping is an important part of the mix engineering workflow that facilitates the process of ... more Subgrouping is an important part of the mix engineering workflow that facilitates the process of manipulating a number of audio tracks simultaneously. We statistically analyse the subgrouping practices of mix engineers in order to establish the relationship between subgrouping and mix preference. We investigate the number of subgroups (relative and absolute), the type of audio processing and the subgrouping strategy in 72 mixes of nine songs, by 16 mix engineers. We analyse the subgrouping setup for each mix of a particular song and also each mix by a particular mixing engineer. We show that subjective preference for a mix strongly correlates with the number of subgroups, and to a lesser extent which types of audio processing are applied to the subgroups.
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