Maham Riaz

This paper reports on a desktop investigation and a lab experiment comparing the video recording capabilities of four commercially available 360-degree cameras: GoPro MAX, Insta360 X3, Garmin VIRB 360, and Ricoh Theta S. The four cameras all use different recording formats and settings and have varying video quality and software support. This makes it difficult to conduct analyses and compare between devices. We have implemented new functions in the Musical Gestures Toolbox (MGT) for reading and merging files from the different platforms. Using the capabilities of FFmpeg, we have also made a new function for converting between different 360-degree video projections and formats. This allows (music) researchers to exploit 360-degree video recordings using regular video-based analysis pipelines.

With the ever-increasing prevalence of technology, its application in various music-related processes, such as music composition and performance, has become increasingly prominent. One fascinating area where technology finds utility is in music performance, offering opportunities for extensive sound exploration and manipulation. In this paper, we introduce an approach utilizing SuperCollider and Open Sound Control (OSC) commands in a multi-speaker setup, enabling spatial audio control for a truly interactive audio spatialization experience. We delve into the musicological dimensions of these distinct methods, examining their integration within a live performance setting to uncover their artistic and expressive potential. By merging technology and musicology, our research aims to unlock new avenues for immersive and captivating musical experiences.

This study aims to use supervised learning ¨C specifically, support vector machines ¨C as a tool for a music mood classification task. Four audio and MIDI datasets, each containing over four hundred files, were composed for use in the training and testing processes. Mood classes were formed according to the valence-arousal plane, resulting in the following: happy, sad, relaxed, and tense. Additional runs were also conducted with the linear discriminant analysis, a dimensionality reduction technique commonly used to better the performance of the classifier. The relevant audio and MIDI features were carefully selected for extraction. MIDI datasets for the same music generated better classification results than corresponding audio datasets. Furthermore, when music is composed with each mood associated with a particular key instead of mixed keys, the classification accuracy is higher.

The paper presents a study of the noise level of accelerometer data from a mobile phone compared to three commercially available IMU-based devices (AX3, Equivital, and Movesense) and a marker-based infrared motion capture system (Qualisys). The sensors are compared in static positions and for measuring human micromotion, with larger motion sequences as reference. The measurements show that all but one of the IMU-based devices capture motion with an accuracy and precision that is far below human micromotion. However, their data and representations differ, so care should be taken when comparing data between devices.

Multi-capsule microphone techniques are used in recording the multi-dimensionality of musical performances. This paper evaluates two such techniques: one developed with the 6-capsule Double Mid-Side-Z (DMS-Z) array, and the other with the 32-capsule spherical array. The recordings underwent a similar process of decoding using the 3DCC method, in which each set of arrays was mapped onto 8 directional signals on the horizontal plane. From the performance aspect, the music ensemble consisted of fo