Download Multimodal Interfaces for Expressive Sound Control This paper introduces research issues on multimodal interaction and interfaces for expressive sound control. We introduce Multisensory Integrated Expressive Environments (MIEEs) as a framework for Mixed Reality applications in the performing arts. Paradigmatic contexts for applications of MIEEs are multimedia concerts, interactive dance / music / video installations, interactive museum exhibitions, distributed cooperative environments for theatre and artistic expression. MIEEs are user-centred systems able to interpret the high-level information conveyed by performers through their expressive gestures and to establish an effective multisensory experience taking into account expressive, emotional, affective content. The lecture discusses some main issues for MIEEs and presents the EyesWeb (www.eyesweb.org) open software platform which has been recently redesigned (version 4) in order to better address MIEE requirements. Short live demonstrations are also presented.
Download Differentiable Attenuation Filters for Feedback Delay Networks We introduce a novel method for designing attenuation filters in
digital audio reverberation systems based on Feedback Delay Networks (FDNs). Our approach uses Second Order Sections (SOS)
of Infinite Impulse Response (IIR) filters arranged as parametric
equalizers (PEQ), enabling fine control over frequency-dependent
reverberation decay. Unlike traditional graphic equalizer designs,
which require numerous filters per delay line, we propose a scalable solution where the number of filters can be adjusted. The frequency, gain, and quality factor (Q) parameters are shared parameters across delay lines and only the gain is adjusted based on delay
length. This design not only reduces the number of optimization
parameters, but also remains fully differentiable and compatible
with gradient-based learning frameworks. Leveraging principles
of analog filter design, our method allows for efficient and accurate filter fitting using supervised learning. Our method delivers
a flexible and differentiable design, achieving state-of-the-art performance while significantly reducing computational cost.
Download A Quadric Surface Model of Vacuum Tubes for Virtual Analog Applications Despite the prevalence of modern audio technology, vacuum tube amplifiers continue to play a vital role in the music industry. For this reason, over the years, many different digital techniques have been introduced for accomplishing their emulation. In this paper, we propose a novel quadric surface model for tube simulations able to overcome the Cardarilli model in terms of efficiency whilst retaining comparable accuracy when grid current is negligible. After showing the model capability to well outline tubes starting from measurement data, we perform an efficiency comparison by implementing the considered tube models as nonlinear 3-port elements in the Wave Digital domain. We do this by taking into account the typical common-cathode gain stage employed in vacuum tube guitar amplifiers. The proposed model turns out to be characterized by a speedup of 4.6× with respect to the Cardarilli model, proving thus to be promising for real-time Virtual Analog applications.
Download Improving Lyrics-to-Audio Alignment Using Frame-wise Phoneme Labels with Masked Cross Entropy Loss This paper addresses the task of lyrics-to-audio alignment, which
involves synchronizing textual lyrics with corresponding music
audio. Most publicly available datasets for this task provide annotations only at the line or word level. This poses a challenge
for training lyrics-to-audio models due to the lack of frame-wise
phoneme labels. However, we find that phoneme labels can be
partially derived from word-level annotations: for single-phoneme
words, all frames corresponding to the word can be labeled with
the same phoneme; for multi-phoneme words, phoneme labels can
be assigned at the first and last frames of the word. To leverage
this partial information, we construct a mask for those frames and
propose a masked frame-wise cross-entropy (CE) loss that considers only frames with known phoneme labels. As a baseline model,
we adopt an autoencoder trained with a Connectionist Temporal
Classification (CTC) loss and a reconstruction loss. We then enhance the training process by incorporating the proposed framewise masked CE loss. Experimental results show that incorporating the frame-wise masked CE loss improves alignment performance. In comparison to other state-of-the art models, our model
provides a comparable Mean Absolute Error (MAE) of 0.216 seconds and a top Median Absolute Error (MedAE) of 0.041 seconds
on the testing Jamendo dataset.
Download Music Emotion Classification: Dataset Acquisition And Comparative Analysis In this paper we present an approach to emotion classification in audio music. The process is conducted with a dataset of 903 clips and mood labels, collected from Allmusic1 database, organized in five clusters similar to the dataset used in the MIREX2 Mood Classification Task. Three different audio frameworks – Marsyas, MIR Toolbox and Psysound, were used to extract several features. These audio features and annotations are used with supervised learning techniques to train and test various classifiers based on support vector machines. To access the importance of each feature several different combinations of features, obtained with feature selection algorithms or manually selected were tested. The performance of the solution was measured with 20 repetitions of 10-fold cross validation, achieving a F-measure of 47.2% with precision of 46.8% and recall of 47.6%.
Download Re-Thinking Sound Separation: Prior Information and Additivity Constraint in Separation Algorithms In this paper, we study the effect of prior information on the quality of informed source separation algorithms. We present results with our system for solo and accompaniment separation and contrast our findings with two other state-of-the art approaches. Results suggest current separation techniques limit performance when compared to extraction process of prior information. Furthermore, we present an alternative view of the separation process where the additivity constraint of the algorithm is removed in the attempt to maximize obtained quality. Plausible future directions in sound separation research are discussed.
Download Automating The Design Of Sound Synthesis Techniques Using Evolutionary Methods Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement processes. A SST is determined by its functional form and internal parameters. Design of SSTs is usually done by selecting a fixed functional form from a handful of commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of inputs + target sound. The approach is capable of suggesting novel functional forms and their internal parameters, suited to follow closely the given examples. Design of a SST is stated as a search problem in the SST space (the space spanned by all the possible valid functional forms and internal parameters, within certain limits to make it practical). This search is done using evolutionary methods; specifically, Genetic Programming (GP).
Download Leveraging Electric Guitar Tones and Effects to Improve Robustness in Guitar Tablature Transcription Modeling Guitar tablature transcription (GTT) aims at automatically generating symbolic representations from real solo guitar performances. Due to its applications in education and musicology, GTT has gained traction in recent years. However, GTT robustness has been limited due to the small size of available datasets. Researchers have recently used synthetic data that simulates guitar performances using pre-recorded or computer-generated tones, allowing for scalable and automatic data generation. The present study complements these efforts by demonstrating that GTT robustness can be improved by including synthetic training data created using recordings of real guitar tones played with different audio effects. We evaluate our approach on a new evaluation dataset with professional solo guitar performances that we composed and collected, featuring a wide array of tones, chords, and scales.
Download Time mosaics - An image processing approach to audio visualization This paper presents a new approach to the visualization of monophonic audio files that simultaneously illustrates general audio properties and the component sounds that comprise a given input file. This approach represents sound clip sequences using archetypal images which are subjected to image processing filters driven by audio characteristics such as power, pitch and signalto-noise ratio. Where the audio is comprised of a single sound it is represented by a single image that has been subjected to filtering. Heterogeneous audio files are represented as a seamless image mosaic along a time axis where each component image in the mosaic maps directly to a discovered component sound. To support this, in a given audio file, the system separates individual sounds and reveals the overlapping period between sound clips. Compared with existing visualization methods such as oscilloscopes and spectrograms, this approach yields more accessible illustrations of audio files, which are suitable for casual and nonexpert users. We propose that this method could be used as an efficient means of scanning audio database queries and navigating audio databases through browsing, since the user can visually scan the file contents and audio properties simultaneously.