Paper Archive

Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack are summed to a single audio bus. This is done so that the mix engineer can apply signal processing to an entire subgroup, speed up the mix work flow and manipulate a number of audio tracks at once. In this work, we investigate which audio features from a set of 159 can be used to automatically subgroup multitrack audio. We determine a subset of audio features from the original 159 audio features to use for automatic subgrouping, by performing feature selection using a Random Forest classifier on a dataset of 54 individual multitracks. We show that by using agglomerative clustering on 5 test multitracks, the entire set of audio features incorrectly clusters 35.08% of the audio tracks, while the subset of audio features incorrectly clusters only 7.89% of the audio tracks. Furthermore, we also show that using the entire set of audio features, ten incorrect subgroups are created. However, when using the subset of audio features, only five incorrect subgroups are created. This indicates that our reduced set of audio features provides a significant increase in classification accuracy for the creation of subgroups automatically.

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To date, the most successful onset detectors are those based on frequency representation of the signal. However, for such methods the time between the physical onset and the reported one is unpredictable and may largely vary according to the type of sound being analyzed. Such variability and unpredictability of spectrum-based onset detectors may not be convenient in some real-time applications. This paper proposes a real-time method to improve the temporal accuracy of state-of-the-art onset detectors. The method is grounded on the theory of hard real-time operating systems where the result of a task must be reported at a certain deadline. It consists of the combination of a time-base technique (which has a high degree of accuracy in detecting the physical onset time but is more prone to false positives and false negatives) with a spectrum-based technique (which has a high detection accuracy but a low temporal accuracy). The developed hard real-time onset detector was tested on a dataset of single non-pitched percussive sounds using the high frequency content detector as spectral technique. Experimental validation showed that the proposed approach was effective in better retrieving the physical onset time of about 50% of the hits detected by the spectral technique, with an average improvement of about 3 ms and maximum one of about 12 ms. The results also revealed that the use of a longer deadline may capture better the variability of the spectral technique, but at the cost of a bigger latency.

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The presented sample library of violin sounds is designed as a tool for the research, development and testing of sound analysis/synthesis algorithms. The library features single sounds which cover the entire frequency range of the instrument in four dynamic levels, two-note sequences for the study of note transitions and vibrato, as well as solo pieces for performance analysis. All parts come with a hand-labeled segmentation ground truth which mark attack, release and transition/transient segments. Additional relevant information on the samples’ properties is provided for single sounds and two-note sequences. Recordings took place in an anechoic chamber with a professional violinist and a recording engineer, using two microphone positions. This document describes the content and the recording setup in detail, alongside basic statistical properties of the data.

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Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents an effective approach based on an Adaptive Network-Based Fuzzy Inference System (ANFIS) for the classification stage required in a speech/music discrimination system. A new simple feature, called Warped LPC-based Spectral Centroid (WLPC-SC), is also proposed. Comparison between WLPC-SC and some of the classical features proposed in [11] is performed, aiming to assess the good discriminatory power of the proposed feature. The length of the vector for describing the proposed psychoacoustic-based feature is reduced to a few statistical values (mean, variance and skewness). To evaluate the performance of the ANFIS system for speech/music discrimination, comparison to other commonly used classifiers is reported. The classification results for different types of music and speech show the good discriminating power of the proposed approach.

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We present a Neural-Driven Multi-Band Processor (NDMP), a differentiable audio processing framework that augments a static sixband Parametric Equalizer (PEQ) with per-band dynamic range compression. We optimize this processor using neural inference for two tasks: Automatic Equalization (AutoEQ), which estimates tonal and dynamic corrections without a reference, and Production Style Transfer (NDMP-ST), which adapts the processing of an input signal to match the tonal and dynamic characteristics of a reference. We train NDMP using a self-supervised strategy, where the model learns to recover a clean signal from inputs degraded with randomly sampled NDMP parameters and gain adjustments. This setup eliminates the need for paired input–target data and enables end-to-end training with audio-domain loss functions. In the inference, AutoEQ enhances previously unseen inputs in a blind setting, while NDMP-ST performs style transfer by predicting taskspecific processing parameters. We evaluate our approach on the MUSDB18 dataset using both objective metrics (e.g., SI-SDR, PESQ, STFT loss) and a listening test. Our results show that NDMP consistently outperforms traditional PEQ and a PEQ+DRC (single-band) baseline, offering a robust neural framework for audio enhancement that combines learned spectral and dynamic control.

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This paper presents a method to detect and distinguish single and multiple audio effects in monophonic electric guitar recordings. It is based on spectral analysis of audio segments located in the sustain part of guitar tones. Overall, 541 spectral, cepstral and harmonic features are extracted from short time spectra of the audio segments. Support Vector Machines are used in combination with feature selection and transform techniques for automatic classification based on the extracted feature vectors. A novel database that consists of approx. 50000 guitar tones was assembled for the purpose of evaluation. Classification accuracy reached 99.2% for the detection and distinction of arbitrary combinations of six frequently used audio effects.

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This study investigates the use of an unsupervised, physicsinformed deep learning framework to model a one-degree-offreedom mass-spring system subjected to a nonlinear friction bow force and governed by a set of ordinary differential equations. Specifically, it examines the application of Physics-Informed Neural Networks (PINNs) and Physics-Informed Deep Operator Networks (PI-DeepONets). Our findings demonstrate that PINNs successfully address the problem across different bow force scenarios, while PI-DeepONets perform well under low bow forces but encounter difficulties at higher forces. Additionally, we analyze the Hessian eigenvalue density and visualize the loss landscape. Overall, the presence of large Hessian eigenvalues and sharp minima indicates highly ill-conditioned optimization. These results underscore the promise of physics-informed deep learning for nonlinear modelling in musical acoustics, while also revealing the limitations of relying solely on physics-based approaches to capture complex nonlinearities. We demonstrate that PI-DeepONets, with their ability to generalize across varying parameters, are well-suited for sound synthesis. Furthermore, we demonstrate that the limitations of PI-DeepONets under higher forces can be mitigated by integrating observation data within a hybrid supervised-unsupervised framework. This suggests that a hybrid supervised-unsupervised DeepONets framework could be a promising direction for future practical applications.

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This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditions and sample rates, we assess the capacity of these models to accurately model the complex behaviours observed in string dynamics. Our findings indicate that our proposed Koopman-based model performs as well as or better than other existing approaches in nonlinear cases for long-sequence modelling. We inform the design of these architectures with the structure of the problems at hand. Although challenges remain in extending model predictions beyond the training horizon (i.e., extrapolation), the focus of our investigation lies in the models’ ability to generalise across different initial conditions within the training time interval. This research contributes insights into the physical modelling of dynamical systems (in particular those addressing musical acoustics) by offering a comparative overview of these and previous methods and introducing innovative strategies for model improvement. Our results highlight the efficacy of these models in simulating non-linear dynamics and emphasise their wide-ranging applicability in accurately modelling dynamical systems over extended sequences.

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Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.

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The proposed automatic target mixing algorithm determines the gains and the equalization settings for the mixing of a multi-track recording using a least-squares optimization. These parameters are estimated using a single channel target mix, that is a signal which contains the same audio tracks as the multi-track recording, but that has been previously mixed using some unknown settings. Several tests have been done in order to evaluate the performances of two different approaches to the optimization, namely the sub-band estimator and the FIR filters estimator. The results show that, using the latter technique, the proposed algorithm is able to retrieve the parameters originally applied to the target mix. This achievement can be useful for remastering applications, where both the original recording sessions and the final mix are available, but there is the need to retrieve the mixing parameters originally applied to the various audio tracks.

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