Download Automatic subgrouping of multitrack audio
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.
Download An Evaluation of Audio Feature Extraction Toolboxes
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.
Download Unsupervised Taxonomy of Sound Effects
Sound effect libraries are commonly used by sound designers in a range of industries. Taxonomies exist for the classification of sounds into groups based on subjective similarity, sound source or common environmental context. However, these taxonomies are not standardised, and no taxonomy based purely on the sonic properties of audio exists. We present a method using feature selection, unsupervised learning and hierarchical clustering to develop an unsupervised taxonomy of sound effects based entirely on the sonic properties of the audio within a sound effect library. The unsupervised taxonomy is then related back to the perceived meaning of the relevant audio features.
Download Objective Evaluations of Synthesised Environmental Sounds
There are a range of different methods for comparing or measuring the similarity between environmental sound effects. These methods can be used as objective evaluation techniques, to evaluate the effectiveness of a sound synthesis method by assessing the similarity between synthesised sounds and recorded samples. We propose to evaluate a number of different synthesis objective evaluation metrics, by using the different distance metrics as fitness functions within a resynthesis algorithm. A recorded sample is used as a target sound, and the resynthesis is intended to produce a set of synthesis parameters that will synthesise a sound as close to the recorded sample as possible, within the restrictions of the synthesis model. The recorded samples are excerpts of selections from a sound effects library, and the results are evaluated through a subjective listening test. Results show that one of the objective function performs significantly worse than several others. Only one method had a significant and strong correlation between the user perceptual distance and the objective distance. A recommendation of an objective evaluation function for measuring similarity between synthesised environmental sounds is made.