Although audio-augmented reality (AAR) has known applications in music, the use of wearables such as augmented reality (AR) glasses for egocentric audio data capture for music has not been investigated. Current egocentric datasets are mostly focused on speech research, neglecting music's unique demands for tasks such as real-time optimisation or assistive listening. This paper introduces EgoMusic, a multimodal dataset featuring synchronised egocentric audio-visual data captured with AR glasses during live performances, alongside studio-quality audio references. We investigate AR glasses' utility for music and baseline artificial intelligence (AI) approaches for hearing enhancement, positioning EgoMusic as the first dataset that enables research for egocentric music AAR.

Sound source separation algorithms have advanced significantly in recent years but many algorithms can suffer from objectionable artefacts. The artefacts include phasiness, transient smearing, high frequency loss, unnatural sounding noise floor and reverberation to name a few. One of the main reasons for this is due to the fact that in many algorithm, individual time-frequency bins are often only attributed to one source at a time, meaning that many time-frequency bins will be set to zero for a separated source. This leads to an impressive signal to interference ratio but at the cost of natural sounding resynthesis. Here, we present a simple algorithm capable of audio inpainting based on self-similarity within the signal. The algorithm attempts to use the non-zero bin values observed in similar frames (past or future) as substitutes for the zero bin values in the current analysis frame. We present results from subjective listening tests which show a preference for the inpainted audio over the original audio produced from a simple source separation algorithm. Further, we use the Fréchet Audio Distance metric to evaluate the perceptual effect of the proposed inpainting algorithm. The results of this evaluation support the subjective test preferences.

Non-negative Matrix Factorization (NMF) has found use in single channel separation of audio signals, as it gives a parts-based decomposition of audio spectrograms where the parts typically correspond to individual notes or chords. However, a notable shortcoming of NMF is the need to cluster the basis functions to their sources after decomposition. Despite recent improvements in algorithms for clustering the basis functions to sources, much work still remains to further improve these algorithms. To this end we present a novel clustering algorithm which overcomes some of the limitations of previous clustering methods. This involves the use of Shifted Non negative Matrix Factorization (SNMF) as a means of clustering the frequency basis functions obtained from NMF. Results show that this gives improved clustering of pitched basis functions over previous methods.

The goal of the Interactive Music Archive Access System (IMAAS) project was to develop an interactive music archive access system which was capable of allowing an end-user to easily extract rhythmic, melodic and harmonic musical metadata descriptors from audio, and allow the user to interact with the archive contents in a manner not typically allowed in archive access systems. To this end, the IMAAS system incorporates a range of real-time interaction tools which allow the user to modify the retrieved audio in a number of ways including the ability to isolate individual instruments in stereo mixes, pitch and time-scale modification, and beat-synchronous looping. This demo gives an overview of the capabilities of the IMAAS application.

A framework is presented which addresses the issues related to the real-time implementation of synchronized video and audio time-scale and pitch-scale modification algorithms. It allows for seamless real-time transition between continually varying, independent time-scale and pitch-scale parameters arising as a result of manual or automatic intervention. We illuminate the problems which arise in a real-time context as well as provide novel solutions to prevent artifacts, minimize latency, and improve synchronization. The time and pitch scaling approach is based on a modified phase vocoder with optional phase locking and an integrated transient detector which enables high-quality transient preservation in real-time. A novel method for audio/visual synchronization was implemented in order to ensure no perceptible latency between audio and video while real-time time scaling and pitch shifting is applied. Evaluation results are reported which demonstrate both high audio quality and minimal synchronization error.

Many digital sound archives still suffer from tremendous problems concerning access. Materials are often in different formats, with related media in separate collections, and with non-standard, specialist, incomplete or even erroneous metadata. Thus, the end user is unable to discover the full value of the archived material. EASAIER addresses these issues with the development of an innovative remote access system which extends beyond standard content management and retrieval systems. The EASAIER system has been designed with sound archives, libraries, museums, broadcast archives, and music schools in mind. However, the tools may be used by anyone interested in accessing archived material; amateur or professional, regardless of the material involved. Furthermore, it enriches the access experience enabling the user to experiment with the materials in exciting new ways. The system features; enhanced cross media retrieval functionality, multi-media synchronisation, audio and video processing, analysis and visualisation tools, all combined within in a single user configurable interface.

Large music collections afford the listener flexibility in the form of choice, which enables the listener to choose the appropriate piece of music to enhance or complement their listening scenario on-demand. However, bundled with such a large music collection is the demanding task of manually searching through each entry in the collection to find the appropriate song required by the listener. This paper highlights the need for contextual and environmental information, which ultimately defines the listenerpsilas listening scenario. Here, the preliminary results of an online music survey are analysed. These results indicate the possibility of how environmental features may be used as metadata to indicate the listenerpsilas mood. Therefore, environmental features, such as location, activity, temperature, lighting and weather have great potential as metadata and hence may be used to create a personalised automatic playlist generator for large music collections.