This article describes the QuBits system, a virtual reality environment offering an expanded medium for musical experience with space and visuals. The user and the computer jointly shape many of the events. Sound engines were designed to explore an aesthetic of algorithmically generated sonic structures, sound mass, interactive evolution, and spatial sound. Real-time challenges are discussed. A network of software is diagramed, solving initial issues with latency. Finally, the principles and methods utilized in the current project are evaluated with implications for future iterations.
In this paper we will perform a preliminary exploration on how neural networks can be used for the task of target-based computer-assisted musical orchestration. We will show how it is possible to model this musical problem as a classification task and we will propose two deep learning models. We will show, first, how they perform as classifiers for musical instrument recognition by comparing them with specific baselines. We will then show how they perform, both qualitatively and quantitatively, in the task of computer-assisted orchestration by comparing them with state-of-the-art systems....
Within the last 15 years, the field of Music Information Retrieval (MIR) has made tremendous progress in the development of algorithms for organizing and analyzing the ever-increasing large and varied amount of music and music-related data available digitally. However, the development of content-based methods to enable or ameliorate multimedia retrieval still remains a central challenge. In this perspective paper, we critically look at the problem of automatic chord estimation from audio recordings as a case study of content-based algorithms, and point out several bottlenecks in current...
This paper introduces OrchideaSOL, a free dataset of samples of extended instrumental playing techniques, designed to be used as default dataset for the Orchidea framework for target-based computer-aided orchestration. OrchideaSOL is a reduced and modified subset of Studio On Line, or SOL for short, a dataset developed at Ircam between 1996 and 1998. We motivate the reasons behind OrchideaSOL and describe the differences between the original SOL and our dataset. We will also show the work done in improving the dynamic ranges of orchestral families and other aspects of the data.
The wavelet scattering transform is an invariant and stable signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks, including PyTorch and TensorFlow/Keras. The transforms are implemented on both CPUs and GPUs, the latter offering a significant speedup over the former. The package also has a small memory footprint. Source code, documentation, and examples are...
Software that provide feedback on music performance are being increasingly used by music students to support their daily practice. The dominant piano performance evaluation models are based on metrics such as playing correct notes and global tempo. However, these metrics are often limited in capturing expressive aspects of performance, which can be seen through slight variations a performer chooses to employ in their playing. In order to capture some of these variations, we propose a web application that serves to compare the user's playing to that of professional recordings of the same...
