We present a method for mapping between the input space of a parametric equaliser and a lower-dimensional representation, whilst preserving the effect’s dependency on the incoming audio signal. The model consists of a parameter weighting stage in which the parameters are scaled to spectral features of the audio signal, followed by a mapping process, in which the equaliser’s 13 inputs are converted to (x, y) coordinates. The model is trained with parameter space data representing two timbral adjectives (warm and bright), measured across a range of musical instrument samples, allowing users to impose a semantically-meaningful timbral modification using the lower-dimensional interface. We test 10 mapping techniques, comprising of dimensionality reduction and reconstruction methods, and show that a stacked autoencoder algorithm exhibits the lowest parameter reconstruction variance, thus providing an accurate map between the input and output space. We demonstrate that the model provides an intuitive method for controlling the audio effect’s parameter space, whilst accurately reconstructing the trajectories of each parameter and adapting to the incoming audio spectrum.

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In this transformation we present a rhythmically constrained audio style transfer technique for automatic mixing and mashing of two audio inputs. In this transformation the rhythmic and timbral features of both input signals are combined together through the use of an audio style transfer process that transforms the files so that they adhere to a larger metrical structure of the chosen input. This is accomplished by finding beat boundaries of both inputs and performing the transformation on beat-length audio segments. In order for the system to perform a mashup between two signals, we reformulate the previously used audio style transfer loss terms into three loss functions and enable them to be independent of the input. We measure and compare rhythmic similarities of the transformed and input audio signals using their rhythmic envelopes to investigate the influence of the tested transformation objectives.

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Many recent approaches to creative transformations of musical audio have been motivated by the success of raw audio generation models such as WaveNet, in which audio samples are modeled by generative neural networks. This paper describes a generative audio synthesis model for multi-drum translation based on a WaveNet denosing autoencoder architecture. The timbre of an arbitrary source audio input is transformed to sound as if it were played by various percussive instruments while preserving its rhythmic structure. Two evaluations of the transformations are conducted based on the capacity of the model to preserve the rhythmic patterns of the input and the audio quality as it relates to timbre of the target drum domain. The first evaluation measures the rhythmic similarities between the source audio and the corresponding drum translations, and the second provides a numerical analysis of the quality of the synthesised audio. Additionally, a semi- and fully-automatic audio effect has been proposed, in which the user may assist the system by manually labelling source audio segments or use a state-of-the-art automatic drum transcription system prior to drum translation.

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Recent advancements in generative audio synthesis have allowed for the development of creative tools for generation and manipulation of audio. In this paper, a strategy is proposed for the synthesis of drum sounds using generative adversarial networks (GANs). The system is based on a conditional Wasserstein GAN, which learns the underlying probability distribution of a dataset compiled of labeled drum sounds. Labels are used to condition the system on an integer value that can be used to generate audio with the desired characteristics. Synthesis is controlled by an input latent vector that enables continuous exploration and interpolation of generated waveforms. Additionally we experiment with a training method that progressively learns to generate audio at different temporal resolutions. We present our results and discuss the benefits of generating audio with GANs along with sound examples and demonstrations.

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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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