Download A Unified Framework for Real-Time Concatenation-Driven Convolution This work introduces a novel framework for Concatenation-Driven Convolution (CDC), unifying concatenative synthesis and real-time convolution into a single integrated audio processing paradigm. While concatenative synthesis has traditionally been used for corpus-based sound generation and convolution has served as a largely static filtering technique, the proposed approach reconceptualizes impulse responses (IRs) as dynamic, navigable sonic material. In the CDC framework, a corpus of audio segments is analyzed using perceptual features and organized via a self-organizing map (SOM), enabling intuitive, gesture-based traversal of a structured timbral space; the resulting concatenative output is treated as a continuously evolving impulse response and injected directly into a partitioned convolution engine. Its central technical contribution is single-engine frequency-domain kernel interpolation: rather than crossfading the outputs of two convolution engines, the FFT-domain kernels of the current and target IRs are interpolated within a single engine, preserving the internal convolution state across IR transitions and avoiding the warm-up energy loss inherent to dual-engine crossfading.
Download The Sounds of the Avian Syrinx - are they Really Flute-Like? This research presents a model of the avian vocal tract, implemented using classical waveguide synthesis and numerical methods. The vocal organ of the songbird, the syrinx, has a unique topography of acoustic tubes (a trachea with a bifurcation at its base) making it a rather unique subject for waveguide synthesis. In the upper region of the two bifid bronchi lies a nonlinear vibrating membrane – the primary resonator in sound production. Unlike most reed musical instruments, the more significant displacement of the membrane is perpendicular to the directions of airflow, due to the Bernoulli effect. The model of the membrane displacement, and the resulting pressure through the constriction created by the membrane motion, is therefore derived beginning with the Bernoulli equation.
Download Sound Transformations Based on the SMS High Level Attributes The basic Spectral Modeling Synthesis (SMS) technique models sounds as the sum of sinusoids plus a residual. Though this analysis/synthesis system has proved to be successful in transforming sounds, more powerful and intuitive musical transformations can be achieved by moving into the SMS high-level attribute plane. In this paper we describe how to extract high level sound attributes from the basic representation, modify them, and add them back before the synthesis stage. In this process new problems come up for which we propose some initial solutions.
Download Symbolic and audio processing to change the expressive intention of a recorded music performance A framework for real-time expressive modification of audio musical performances is presented. An expressiveness model compute the deviations of the musical parameters which are relevant in terms of control of the expressive intention. The modifications are then realized by the integration of the model with a sound processing engine.
Download Realtime Multiple-Pitch and Multiple-Instrument Recognition for Music Signals Using Sparse Non-Negative Constraints In this paper we introduce a simple and fast method for realtime recognition of multiple pitches produced by multiple musical instruments. Our proposed method is based on two important facts: (1) that timbral information of any instrument is pitch-dependant and (2) that the modulation spectrum of the same pitch seems to result into a persistent representation of the characteristics of the instrumental family. Using these basic facts, we construct a learning algorithm to obtain pitch templates of all possible notes on various instruments and then devise an online algorithm to decompose a realtime audio buffer using the learned templates. The learning and decomposition proposed here are inspired by non-negative matrix factorization methods but differ by introduction of an explicit sparsity control. Our test results show promising recognition rates for a realtime system on real music recordings. We discuss further improvements that can be made over the proposed system.
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 Sound Effects Dataset Unification With the Universal Category System Sound effects (SFX) datasets and libraries often employ distinct tagging schemes, taxonomies, and metadata structures. This creates challenges for research on SFX classification and generation because incompatible taxonomies lead to siloed datasets that might require individualized approaches, result in non-comparable outcomes, and prevent data merging strategies. We propose a modular dataset relabeling framework that adopts the Universal Category System (UCS), an industry-standard hierarchical taxonomy for sound effects, as a shared structural foundation. This open-source framework enables us (i) to convert tags of existing datasets to UCS with a rule-based multi-stage pipeline and conflict resolution to achieve high automatic conversion rates, (ii) to suggest a stratified dataset split for the new labels, and (iii) to combine multiple datasets. To showcase the practical utility, we introduce the EnvSound-UCS dataset, a publicly available unified UCS-compliant dataset of environmental sounds with 58,057 sound clips from three sources: AudioSet, FSD50K, and ESC-50.
Download Multi-channel Audio Information Hiding We consider a method of hiding many audio channels in one host signal. The purpose of this is to provide a ‘mix’ that incorporates information on all the channels used to produce it, thereby allowing all, or, at least some channels to be stored in the mix for later use (e.g. for re-mixing and/or archiving). After providing an overview of some recently published audio water marking schemes in the time and transform domains, we present a method that is based on using a four least significant bits scheme to embed five MP3 files into a single 16-bit host WAV file without incurring any perceptual audio distortions in either the host data or embedded files. The host WAV file is taken to be the final mix associated with the original multi-channel data before applying minimal MP3 compression (WAV to MP3 conversion), or, alternatively, an arbitrary host WAV file into which other multi-channel data in an MP3 format is hidden. The embedded information can be encrypted and/or the embedding locations randomized on a channelby-channel basis depending on the security protocol desired by the user. The method is illustrated by providing example m-code for interested readers to reproduce the results obtained to date and as a basis for further development.
Download Adaptive Harmonization and Pitch Correction of Polyphonic Audio Using Spectral Clustering There are several well known harmonization and pitch correction techniques that can be applied to monophonic sound sources. They are based on automatic pitch detection and frequency shifting without time stretching. In many applications it is desired to apply such effects on the dominant melodic instrument of a polyphonic audio mixture. However, applying them directly to the mixture results in artifacts, and automatic pitch detection becomes unreliable. In this paper we describe how a dominant melody separation method based on spectral clustering of sinusoidal peaks can be used for adaptive harmonization and pitch correction in mono polyphonic audio mixtures. Motivating examples from a violin tutoring perspective as well as modifying the saxophone melody of an old jazz mono recording are presented.
Download Identification of individual guitar sounds by support vector machines This paper introduces an automatic classification system for the identification of individual classical guitars by single notes played on these guitars. The classification is performed by Support Vector Machines (SVM) that have been trained with the features of the single notes. The features used for classification were the time series of the partial tones, the time series of the MFCCs (Mel Frequency Cepstral Coefficients), and the “nontonal” contributions to the spectrum. The influences of these features on the classification success are reported. With this system, 80% of the sounds recorded with three different guitars were classified correctly. A supplementary classification experiment was carried out with human listeners resulting in a rate of 65% of correct classifications.