Download Pulsetable Synthesis of Wind Instrument Tones We revisit pulsetable synthesis, an efficient technique for generating plausible and expressive wind instrument tones. Based on the principles of pulse forming theory, this method models sound production as the periodic repetition of shaped pulses characterizing the target instruments' spectral envelope. In this approach, single-cycle waveforms, referred to as pulses, are stored in pulsetables indexed by their corresponding fundamental frequency. During synthesis, the pulses are read from these tables to form a periodic waveform, which is further shaped by time-varying low-pass filtering, amplification, and reverberation. These processes are guided by control signal contours that describe how fundamental frequency, brightness, and loudness evolve over time. Through case studies with real-world wind instrument recordings, we show how the interplay between these control signals gives rise to articulations such as attack transients, vibrato, and growl. Finally, we discuss the potential of this framework for integration into Differentiable Digital Signal Processing (DDSP) models, where neural networks could learn synthesis parameters directly from training data.
Download Adapting Diffusion-Based Music Synthesis to Speech and Singing Voice Conversion Recent diffusion-based generative models have achieved strong results in domain-specific audio generation tasks such as speech, singing, and instrumental music synthesis. However, these models are typically specialized and do not generalize well to mixed or intermediate audio types. In this work, we adapt a diffusion-based model originally designed for multi-instrument music synthesis to voice conversion, covering both speech and singing within a unified framework. Specifically, we extend musical note-based conditioning to include phonetic posteriorgrams (PPGs) and pitch contours, and reinterpret timbre conditioning as speaker or singer identity via feature-wise linear modulation. Experiments show that the adapted model matches or surpasses a dedicated voice conversion system in terms of naturalness and performer similarity, while maintaining accurate pitch control across speech and singing. At the same time, we observe limitations in phonetic fidelity and a degradation in vocal quality when incorporating instrumental training data. Furthermore, we demonstrate that off-the-shelf feature extractors provide effective conditioning signals, enabling large-scale self-supervised training without manual annotations. These results highlight the potential of cross-domain model transfer towards unified audio generation systems capable of handling speech, singing, and music.
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 Real-Time Transcription and Separation of Drum Recordings Based on NMF Decompositon This paper proposes a real-time capable method for transcribing and separating occurrences of single drum instruments in polyphonic drum recordings. Both the detection and the decomposition are based on Non-Negative Matrix Factorization and can be implemented with very small systemic delay. We propose a simple modification to the update rules that allows to capture timedynamic spectral characteristics of the involved drum sounds. The method can be applied in music production and music education software. Performance results with respect to drum transcription are presented and discussed. The evaluation data-set consisting of annotated drum recordings is published for use in further studies in the field. Index Terms - drum transcription, source separation, nonnegative matrix factorization, spectral processing, audio plug-in, music production, music education
Download Effective Singing Voice Detection in Popular Music Using ARMA Filtering Locating singing voice segments is essential for convenient indexing, browsing and retrieval large music archives and catalogues. Furthermore, it is beneficial for automatic music transcription and annotations. The approach described in this paper uses Mel-Frequency Cepstral Coefficients in conjunction with Gaussian Mixture Models for discriminating two classes of data (instrumental music and singing voice with music background). Due to imperfect classification behavior, the categorization without additional post-processing tends to alternate within a very short time span, whereas singing voice tends to be continuous for several frames. Thus, various tests have been performed to identify a suitable decision function and corresponding smoothing methods. Results are reported by comparing the performance of straightforward likelihood based classifications vs. postprocessing with an autoregressive moving average filtering method.
Download NMF Toolbox: Music Processing Applications of Nonnegative Matrix Factorization Nonnegative matrix factorization (NMF) is a family of methods widely used for information retrieval across domains including text, images, and audio. Within music processing, NMF has been used for tasks such as transcription, source separation, and structure analysis. Prior work has shown that initialization and constrained update rules can drastically improve the chances of NMF converging to a musically meaningful solution. Along these lines we present the NMF toolbox, containing MATLAB and Python implementations of conceptually distinct NMF variants—in particular, this paper gives an overview for two algorithms. The first variant, called nonnegative matrix factor deconvolution (NMFD), extends the original NMF algorithm to the convolutive case, enforcing the temporal order of spectral templates. The second variant, called diagonal NMF, supports the development of sparse diagonal structures in the activation matrix. Our toolbox contains several demo applications and code examples to illustrate its potential and functionality. By providing MATLAB and Python code on a documentation website under a GNU-GPL license, as well as including illustrative examples, our aim is to foster research and education in the field of music processing.