In addition to tone, pitch and rhythm, dynamics is one of the expressive dimensions of the performance of a music piece that has received limited attention. While the usage of dynamics may vary from artist to artist, and also from performance to performance, a systematic methodology to automatically identify the dynamics of a performance in terms of musically meaningful terms like forte, piano may offer valuable feedback in the context of music education and in particular in singing. To this end, we have manually annotated the dynamic markings of commercial recordings of popular rock and pop songs from the Smule Vocal Balanced (SVB) dataset which will be used as reference data. Then as a first step for our research goal, we propose a method to derive and compare singing voice loudness curves in polyphonic mixtures. Towards measuring the similarity and variation of dynamics, we compare the dynamics curves of the SVB renditions with the one derived from the original songs. We perform the same comparison using professionally produced renditions from a karaoke website. We relate high values of Spearman correlation coefficient found in some select student renditions and the professional renditions with accurate dynamics.

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The process of creating drum sounds has seen significant evolution in the past decades. The development of analogue drum synthesizers, such as the TR-808, and modern sound design tools in Digital Audio Workstations led to a variety of drum timbres that defined entire musical genres. Recently, drum synthesis research has been revived with a new focus on training generative neural networks to create drum sounds. Different interfaces have previously been proposed to control the generative process, from low-level latent space navigation to high-level semantic feature parameterisation, but no comprehensive analysis has been presented to evaluate how each approach relates to the creative process. We aim to evaluate how different interfaces support creative control over drum generation by conducting a user study based on the Creative Support Index. We experiment with both a supervised method that decodes semantic latent space directions and an unsupervised Closed-Form Factorization approach from computer vision literature to parameterise the generation process and demonstrate that the latter is the preferred means to control a drum synthesizer based on the StyleGAN2 network architecture.

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Many electronic music (EM) genres are composed through the activation of short audio recordings of instruments designed for seamless repetition—or loops. In this work, loops of key structural groups such as bass, percussive or melodic elements are labelled by the role they occupy in a piece of music through the task of automatic instrumentation role classification (AIRC). Such labels assist EM producers in the identification of compatible loops in large unstructured audio databases. While human annotation is often laborious, automatic classification allows for fast and scalable generation of these labels. We experiment with several deeplearning architectures and propose a data augmentation method for improving multi-label representation to balance classes within the Freesound Loop Dataset. To improve the classification accuracy of the architectures, we also evaluate different pooling operations. Results indicate that in combination with the data augmentation and pooling strategies, the proposed system achieves state-of-theart performance for AIRC. Additionally, we demonstrate how our proposed AIRC method is useful for analysing the structure of EM compositions through loop activation transcription.

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