Download Discrete Implementation Of The First Order System Cascade As The Basis For A Melodic Segmentation Model The basis for a low-level melodic segmentation model and its discrete implementation is presented. The model is based on the discrete approximation of the one-dimensional convective transport mechanism. In this way, a physically plausible mechanism for achieving multi-scale representation is obtained. Some aspects of edge detection theory thought to be relevant for solving similar problems in auditory perception are briefly introduced. Two examples presenting the dynamic behaviour of the model are shown.
Download Polyphonic music analysis by signal processing and support vector machines In this paper an original system for the analysis of harmony and polyphonic music is introduced. The system is based on signal processing and machine learning. A new multi-resolution, fast analysis method is conceived to extract time-frequency energy spectrum at the signal processing stage, while support vector machine is used as machine learning technology. Aiming at the analysis of rather general audio content, experiments are made on a huge set of recorded samples, using 19 music instruments combined together or alone, with different polyphony. Experimental results show that fundamental frequencies are detected with a remarkable success ratio and that the method can provide excellent results in general cases.
Download Vocal Tract Area Estimation by Gradient Descent Articulatory features can provide interpretable and flexible controls for the synthesis of human vocalizations by allowing the user to directly modify parameters like vocal strain or lip position. To make this manipulation through resynthesis possible, we need to estimate the features that result in a desired vocalization directly from audio recordings. In this work, we propose a white-box optimization technique for estimating glottal source parameters and vocal tract shapes from audio recordings of human vowels. The approach is based on inverse filtering and optimizing the frequency response of a waveguide model of the vocal tract with gradient descent, propagating error gradients through the mapping of articulatory features to the vocal tract area function. We apply this method to the task of matching the sound of the Pink Trombone, an interactive articulatory synthesizer, to a given vocalization. We find that our method accurately recovers control functions for audio generated by the Pink Trombone itself. We then compare our technique against evolutionary optimization algorithms and a neural network trained to predict control parameters from audio. A subjective evaluation finds that our approach outperforms these black-box optimization baselines on the task of reproducing human vocalizations.
Download Dimensionality Reduction Techniques for Fear Emotion Detection from Speech In this paper, we propose to reduce the relatively high-dimension of pitch-based features for fear emotion recognition from speech. To do so, the K-nearest neighbors algorithm has been used to classify three emotion classes: fear, neutral and ’other emotions’. Many techniques of dimensionality reduction are explored. First of all, optimal features ensuring better emotion classification are determined. Next, several families of dimensionality reduction, namely PCA, LDA and LPP, are tested in order to reveal the suitable dimension range guaranteeing the highest overall and fear recognition rates. Results show that the optimal features group permits 93.34% and 78.7% as overall and fear accuracy rates respectively. Using dimensionality reduction, Principal Component Analysis (PCA) has given the best results: 92% as overall accuracy rate and 93.3% as fear recognition percentage.
Download Subjective Evaluation of Sound Quality and Control of Drum Synthesis with Stylewavegan In this paper we investigate into perceptual properties of StyleWaveGAN, a drum synthesis method proposed in a previous publication. For both, the sound quality as well as the control precision StyleWaveGAN has been shown to deliver state of the art performance for quantitative metrics (FAD and MSE of the control parameters). The present paper aims to provide insight into the perceptual relevance of these results. Accordingly, we performed a subjective evaluation of the sound quality as well as a subjective evaluation of the precision of the control using timbre descriptors from the AudioCommons toolbox. We evaluate the sound quality with mean opinion score and make measurements of psychophysical response to the variations of the control. By means of the perceptual tests, we demonstrate that StyleWaveGAN produces better sound quality than state-of-the-art model DrumGAN and that the mean control error is lower than the absolute threshold of perception at every point of measurement used in the experiment.
Download Improved Automatic Instrumentation Role Classification and Loop Activation Transcription Many electronic music (EM) genres are composed through the activation of short audio recordings of instruments designed for seamless repetition—or loops. In this work, loops of key structural groups such as bass, percussive or melodic elements are labelled by the role they occupy in a piece of music through the task of automatic instrumentation role classification (AIRC). Such labels assist EM producers in the identification of compatible loops in large unstructured audio databases. While human annotation is often laborious, automatic classification allows for fast and scalable generation of these labels. We experiment with several deeplearning architectures and propose a data augmentation method for improving multi-label representation to balance classes within the Freesound Loop Dataset. To improve the classification accuracy of the architectures, we also evaluate different pooling operations. Results indicate that in combination with the data augmentation and pooling strategies, the proposed system achieves state-of-theart performance for AIRC. Additionally, we demonstrate how our proposed AIRC method is useful for analysing the structure of EM compositions through loop activation transcription.
Download Notes on the use of Variational Autoencoders for Speech and Audio Spectrogram Modeling Variational autoencoders (VAEs) are powerful (deep) generative artificial neural networks. They have been recently used in several papers for speech and audio processing, in particular for the modeling of speech/audio spectrograms. In these papers, very poor theoretical support is given to justify the chosen data representation and decoder likelihood function or the corresponding cost function used for training the VAE. Yet, a nice theoretical statistical framework exists and has been extensively presented and discussed in papers dealing with nonnegative matrix factorization (NMF) of audio spectrograms and its application to audio source separation. In the present paper, we show how this statistical framework applies to VAE-based speech/audio spectrogram modeling. This provides the latter insights on the choice and interpretability of data representation and model parameterization.
Download Quality Audio Prototyping: A Prototype System for Unified Sound Retrieval and Procedural Generation This paper presents Quality Audio Prototyping (QAP), a unified prototype system for sound retrieval and procedural generation. The system is designed to support rapid exploration of sound effects through a common interface that combines retrieval from existing audio collections with controllable procedural synthesis. By bringing these two paradigms together, QAP allows users to search for recorded sounds, generate new material, and iteratively refine results within a single workflow. The prototype emphasizes usability, extensibility, and practical sound-design applications, providing a foundation for future work on integrated retrieval and generation systems.
Download Maximum Filter Vibrato Suppression for Onset Detection We present SuperFlux - a new onset detection algorithm with vibrato suppression. It is an enhanced version of the universal spectral flux onset detection algorithm, and reduces the number of false positive detections considerably by tracking spectral trajectories with a maximum filter. Especially for music with heavy use of vibrato (e.g., sung operas or string performances), the number of false positive detections can be reduced by up to 60% without missing any additional events. Algorithm performance was evaluated and compared to state-of-the-art methods on the basis of three different datasets comprising mixed audio material (25,927 onsets), violin recordings (7,677 onsets) and operatic solo voice recordings (1,448 onsets). Due to its causal nature, the algorithm is applicable in both offline and online real-time scenarios.
Download A DDSP Framework for Adaptive Room Equalization Adaptive room equalization remains challenging under time-varying acoustic conditions and complex excitation signals, such as music. In these scenarios, classical filtered-x least mean squares (Fx-LMS) methods falter due to their rigid formulation. We present a modular differentiable digital signal processing (DDSP) framework for closed-loop adaptive room equalization that recovers Fx-LMS as a special case through automatic differentiation. The framework supports interchangeable EQ structures, response estimation methods, loss functions, and optimizers. Experiments with time-varying measured room impulse responses show that frequency-domain objectives provide more stable adaptation than time-domain objectives in the considered scenarios. Relative to the non-equalized response, system distance is reduced by 70% and mel-spectral distance by 13% (worst-case scenario). We further examine how online room response estimation accuracy and frame length affect the trade-off between responsiveness and convergence stability. Overall, the framework provides a unified open-source basis for exploring synergies between classical adaptive filtering and DDSP-based optimization.