Download TSM Toolbox: MATLAB Implementations of Time-Scale Modification Algorithms Time-scale modification (TSM) algorithms have the purpose of stretching or compressing the time-scale of an input audio signal without altering its pitch. Such tools are frequently used in scenarios like music production or music remixing. There exists a large variety of different algorithmic approaches to TSM, all of them having their very own advantages and drawbacks. In this paper, we present the TSM toolbox, which contains MATLAB implementations of several conceptually different TSM algorithms. In particular, our toolbox provides the code for a recently proposed TSM approach, which integrates different classical TSM algorithms in combination with harmonic-percussive source separation (HPSS). Furthermore, our toolbox contains several demo applications and additional code examples. Providing MATLAB code on a well-documented website under a GNU-GPL license and including illustrative examples, our aim is to foster research and education in the field of audio processing.
Download Adaptive Pitch-Shifting With Applications to Intonation Adjustment in a Cappella Recordings A central challenge for a cappella singers is to adjust their intonation and to stay in tune relative to their fellow singers. During
editing of a cappella recordings, one may want to adjust local intonation of individual singers or account for global intonation drifts
over time. This requires applying a time-varying pitch-shift to the
audio recording, which we refer to as adaptive pitch-shifting. In
this context, existing (semi-)automatic approaches are either laborintensive or face technical and musical limitations. In this work,
we present automatic methods and tools for adaptive pitch-shifting
with applications to intonation adjustment in a cappella recordings. To this end, we show how to incorporate time-varying information into existing pitch-shifting algorithms that are based on
resampling and time-scale modification (TSM). Furthermore, we
release an open-source Python toolbox, which includes a variety
of TSM algorithms and an implementation of our method. Finally,
we show the potential of our tools by two case studies on global
and local intonation adjustment in a cappella recordings using a
publicly available multitrack dataset of amateur choral singing.
Download A Real-Time Approach for Estimating Pulse Tracking Parameters for Beat-Synchronous Audio Effects Predominant Local Pulse (PLP) estimation, an established method for extracting beat positions and other periodic pulse information from audio signals, has recently been extended with an online variant tailored for real-time applications. In this paper, we introduce a novel approach to generating various real-time control signals from the original online PLP output. While the PLP activation function encodes both predominant pulse information and pulse stability, we propose several normalization procedures to discern local pulse oscillation from stability, utilizing the PLP activation envelope. Through this, we generate pulse-synchronous Low Frequency Oscillators (LFOs) and supplementary confidence-based control signals, enabling dynamic control over audio effect parameters in real-time. Additionally, our approach enables beat position prediction, providing a look-ahead capability, for example, to compensate for system latency. To showcase the effectiveness of our control signals, we introduce an audio plugin prototype designed for integration within a Digital Audio Workstation (DAW), facilitating real-time applications of beat-synchronous effects during live mixing and performances. Moreover, this plugin serves as an educational tool, providing insights into PLP principles and the tempo structure of analyzed music signals.
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 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.