Publications

2017

Marc Ciufo Green and Damian Murphy,
EigenScape: A Database of Spatial Acoustic Scene Recordings,
Applied Sciences, Special Issue on Sound and Music Computing

The classification of acoustic scenes and events is an emerging area of research in the field of machine listening. Most of the research conducted so far uses spectral features extracted from monaural or stereophonic audio rather than spatial features extracted from multichannel recordings. This is partly due to the lack thus far of a substantial body of spatial recordings of acoustic scenes. This paper formally introduces EigenScape, a new database of fourth-order Ambisonic recordings of eight different acoustic scene classes. The potential applications of a spatial machine listening system are discussed before detailed information on the recording process and dataset are provided. A baseline spatial classification system using directional audio coding (DirAC) techniques is detailed and results from this classifier are presented. The classifier is shown to give good overall scene classification accuracy across the dataset, with 7 of 8 scenes being classified with an accuracy of greater than 60% with an 11% improvement in overall accuracy compared to use of Mel-frequency cepstral coefficient (MFCC) features. Further analysis of the results shows potential improvements to the classifier. It is concluded that the results validate the new database and show that spatial features can characterise acoustic scenes and as such are worthy of further investigation.

Additional materials:


Marc Ciufo Green and Damian Murphy,
Acoustic Scene Classification Using Spatial Features,
DCASE Workshop 2017.

The first paper to be published from my PhD work. This paper introduces the EigenScape database of 4th-order Ambisonic acoustic scene recordings I have made. In this paper, I show that spatial audio features extracted using Directional Audio Coding (DirAC) techniques outperform the Mel-Frequency Cepstral Coefficient (MFCC) features commonly used for machine listening to classify the acoustic scenes in the recordings. This is an important result as it is the first study to show the viability of spatial features for acoustic scene analysis. The differences in label confusion between the MFCC and DirAC are especially interesting, as these suggest that certain scenes that are spectrally similar might not necessarily be spatially similar.


2016

Marc Ciufo Green, John Szymanski and Matt Speed,
Assessing the Suitability of the Magnitude Slope Deviation Detection Criterion for use in Automatic Feedback Control,
DAFx-16.

My Master’s research project was conducted in conjunction with Allen & Heath and investigated automatic feedback prevention systems. In this paper based on the results from that study, I show that the MSD criterion – a method for feedback detection originally developed for aircraft comms. systems – could potentially also be a useful method for live sound scenarios. The system works well for classical music and speech, but unfortunately not for rock music, so it wasn’t incorporated into future A&H products.

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