Download Wave Digital Modeling of Circuits with Multiple One-Port Nonlinearities Based on Lipschitz-Bounded Neural Networks Neural networks have found application within the Wave Digital Filters (WDFs) framework as data-driven input-output blocks for modeling single one-port or multi-port nonlinear devices in circuit systems. However, traditional neural networks lack predictable bounds for their output derivatives, essential to ensure convergence when simulating circuits with multiple nonlinear elements using fixed-point iterative methods, e.g., the Scattering Iterative Method (SIM). In this study, we address such issue by employing Lipschitz-bounded neural networks for regressing nonlinear WD scattering relations of one-port nonlinearities.
Download Beat histogram features for rhythm-based musical genre classification using multiple novelty functions In this paper we present beat histogram features for multiple level rhythm description and evaluate them in a musical genre classification task. Audio features pertaining to various musical content categories and their related novelty functions are extracted as a basis for the creation of beat histograms. The proposed features capture not only amplitude, but also tonal and general spectral changes in the signal, aiming to represent as much rhythmic information as possible. The most and least informative features are identified through feature selection methods and are then tested using Support Vector Machines on five genre datasets concerning classification accuracy against a baseline feature set. Results show that the presented features provide comparable classification accuracy with respect to other genre classification approaches using periodicity histograms and display a performance close to that of much more elaborate up-to-date approaches for rhythm description. The use of bar boundary annotations for the texture frames has provided an improvement for the dance-oriented Ballroom dataset. The comparably small number of descriptors and the possibility of evaluating the influence of specific signal components to the general rhythmic content encourage the further use of the method in rhythm description tasks.
Download A Structural Similarity Index Based Method to Detect Symbolic Monophonic Patterns in Real-Time Automatic detection of musical patterns is an important task in the field of Music Information Retrieval due to its usage in multiple applications such as automatic music transcription, genre or instrument identification, music classification, and music recommendation. A significant sub-task in pattern detection is the realtime pattern detection in music due to its relevance in application domains such as the Internet of Musical Things. In this study, we present a method to identify the occurrence of known patterns in symbolic monophonic music streams in real-time. We introduce a matrix-based representation to denote musical notes using its pitch, pitch-bend, amplitude, and duration. We propose an algorithm based on an independent similarity index for each note attribute. We also introduce the Match Measure, which is a numerical value signifying the degree of the match between a pattern and a sequence of notes. We have tested the proposed algorithm against three datasets: a human recorded dataset, a synthetically designed dataset, and the JKUPDD dataset. Overall, a detection rate of 95% was achieved. The low computational load and minimal running time demonstrate the suitability of the method for real-world, real-time implementations on embedded systems.
Download Musical Instrument Identification in Continuous Recordings Recognition of musical instruments in multi-instrumental, polyphonic music is a difficult challenge which is yet far from being solved. Successful instrument recognition techniques in solos (monophonic or polyphonic recordings of single instruments) can help to deal with this task. We introduce an instrument recognition process in solo recordings of a set of instruments (bassoon, clarinet, flute, guitar, piano, cello and violin), which yields a high recognition rate. A large and very diverse solo database (108 different solos, all by different performers) is used in order to encompass the different sound possibilities of each instrument and evaluate the generalization abilities of the classification process. First we bring classification results using a very extensive collection of features (62 different feature types), and then use our GDE feature selection algorithm to select a smaller feature set with a relatively short computation time, which allows us to perform instrument recognition in solos in real-time, with only a slight decrease in recognition rate. We demonstrate that our real-time solo classifier can also be useful for instrument recognition in duet performances, and improved using simple “source reduction”.
Download On the Challenges of Embedded Real-Time Music Information Retrieval Real-time applications of Music Information Retrieval (MIR) have been gaining interest as of recently. However, as deep learning becomes more and more ubiquitous for music analysis tasks, several challenges and limitations need to be overcome to deliver accurate and quick real-time MIR systems. In addition, modern embedded computers offer great potential for compact systems that use MIR algorithms, such as digital musical instruments. However, embedded computing hardware is generally resource constrained, posing additional limitations. In this paper, we identify and discuss the challenges and limitations of embedded real-time MIR. Furthermore, we discuss potential solutions to these challenges, and demonstrate their validity by presenting an embedded real-time classifier of expressive acoustic guitar techniques. The classifier achieved 99.2% accuracy in distinguishing pitched and percussive techniques and a 99.1% average accuracy in distinguishing four distinct percussive techniques with a fifth class for pitched sounds. The full classification task is a considerably more complex learning problem, with our preliminary results reaching only 56.5% accuracy. The results were produced with an average latency of 30.7 ms.
Download Antialiased Black-Box Modeling of Audio Distortion Circuits Using Real Linear Recurrent Units In this paper, we propose the use of real-valued Linear Recurrent
Units (LRUs) for black-box modeling of audio circuits. A network architecture composed of real LRU blocks interleaved with
nonlinear processing stages is proposed.
Two case studies are
presented, a second-order diode clipper and an overdrive distortion pedal. Furthermore, we show how to integrate the antiderivative antialiaisng technique into the proposed method, effectively
lowering oversampling requirements. Our experiments show that
the proposed method generates models that accurately capture the
nonlinear dynamics of the examined devices and are highly efficient, which makes them suitable for real-time operation inside
Digital Audio Workstations.
Download End-to-end equalization with convolutional neural networks This work aims to implement a novel deep learning architecture to perform audio processing in the context of matched equalization. Most existing methods for automatic and matched equalization show effective performance and their goal is to find a respective transfer function given a frequency response. Nevertheless, these procedures require a prior knowledge of the type of filters to be modeled. In addition, fixed filter bank architectures are required in automatic mixing contexts. Based on end-to-end convolutional neural networks, we introduce a general purpose architecture for equalization matching. Thus, by using an end-toend learning approach, the model approximates the equalization target as a content-based transformation without directly finding the transfer function. The network learns how to process the audio directly in order to match the equalized target audio. We train the network through unsupervised and supervised learning procedures. We analyze what the model is actually learning and how the given task is accomplished. We show the model performing matched equalization for shelving, peaking, lowpass and highpass IIR and FIR equalizers.
Download Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial Approaches Accurately estimating nonlinear audio effects without access to
paired input-output signals remains a challenging problem. This
work studies unsupervised probabilistic approaches for solving this
task. We introduce a method, novel for this application, based
on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using blackand gray-box models. This study compares this method with a
previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the
effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show
that the diffusion-based approach provides more stable results and
is less sensitive to data availability, while the adversarial approach
is superior at estimating more pronounced distortion effects. Our
findings contribute to the robust unsupervised blind estimation of
audio effects, demonstrating the potential of diffusion models for
system identification in music technology.
Download One-to-Many Conversion for Percussive Samples A filtering algorithm for generating subtle random variations in
sampled sounds is proposed. Using only one recording for impact
sound effects or drum machine sounds results in unrealistic repetitiveness during consecutive playback. This paper studies spectral
variations in repeated knocking sounds and in three drum sounds:
a hihat, a snare, and a tomtom. The proposed method uses a short
pseudo-random velvet-noise filter and a low-shelf filter to produce
timbral variations targeted at appropriate spectral regions, yielding potentially an endless number of new realistic versions of a
single percussive sampled sound.
The realism of the resulting
processed sounds is studied in a listening test. The results show
that the sound quality obtained with the proposed algorithm is at
least as good as that of a previous method while using 77% fewer
computational operations. The algorithm is widely applicable to
computer-generated music and game audio.
Download Inference-Time Structured Pruning for Real-Time Neural Network Audio Effects Structured pruning is a technique for reducing the computational
load and memory footprint of neural networks by removing structured subsets of parameters according to a predefined schedule
or ranking criterion.
This paper investigates the application of
structured pruning to real-time neural network audio effects, focusing on both feedforward networks and recurrent architectures.
We evaluate multiple pruning strategies at inference time, without retraining, and analyze their effects on model performance. To
quantify the trade-off between parameter count and audio fidelity,
we construct a theoretical model of the approximation error as a
function of network architecture and pruning level. The resulting bounds establish a principled relationship between pruninginduced sparsity and functional error, enabling informed deployment of neural audio effects in constrained real-time environments.