Paper Archive

Relative music loudness estimation is a MIR task that consists in dividing audio in segments of three classes: Foreground Music, Background Music and No Music. Given the temporal correlation of music, in this work we approach the task using a type of network with the ability to model temporal context: the Temporal Convolutional Network (TCN). We propose two architectures: a TCN, and a novel architecture resulting from the combination of a TCN with a Convolutional Neural Network (CNN) front-end. We name this new architecture CNN-TCN. We expect the CNN front-end to work as a feature extraction strategy to achieve a more efficient usage of the network’s parameters. We use the OpenBMAT dataset to train and test 40 TCN and 80 CNN-TCN models with two grid searches over a set of hyper-parameters. We compare our models with the two best algorithms submitted to the tasks of music detection and relative music loudness estimation in MIREX 2019. All our models outperform the MIREX algorithms even when using a lower number of parameters. The CNN-TCN emerges as the best architecture as all its models outperform all TCN models. We show that adding a CNN front-end to a TCN can actually reduce the number of parameters of the network while improving performance. The CNN front-end effectively works as a feature extractor producing consistent patterns that identify different combinations of music and non-music sounds and also helps in producing a smoother output in comparison to the TCN models.

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In this paper a system for continuous analysis, visualization and classification of musical streams is proposed. The system performs visualization and classification task by means of three high-level, semantic features extracted computing a reduction on a multidimensional low-level feature vector through the usage of Gaussian Mixture Models. The visualization of the semantic characteristics of the audio stream has been implemented by mapping the value of the high-level features on a triangular plot and by assigning to each feature a primary color. In this manner, besides having the representation of musical evolution of the signal, we have also obtained representative colors for each musical part of the analyzed streams. The classification exploits a set of one-against-one threedimensional Support Vector Machines trained on some target genres. The obtained results on visualization and classification tasks are very encouraging: our tests on heterogeneous genre streams have shown the validity of proposed approach.

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In this paper we present a new method for the pseudo black-box modelling of general continuous-time state-space systems using a discrete-time state-space system with an embedded deep neural network. Examples are given of how this method can be applied to a number of common nonlinear electronic circuits used in music technology, namely two kinds of diode-based guitar distortion circuits and the lowpass filter of the Korg MS-20 synthesizer.

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Piano transcription is a classic problem in music information retrieval. More and more transcription methods based on deep learning have been proposed in recent years. In 2019, Google Brain published a larger piano transcription dataset, MAESTRO. On this dataset, Onsets and Frames transcription approach proposed by Hawthorne achieved a stunning onset F1 score of 94.73%. Unlike the annotation method of Onsets and Frames, Transition-aware model presented in this paper annotates the attack process of piano signals called atack transition in multiple frames, instead of only marking the onset frame. In this way, the piano signals around onset time are taken into account, enabling the detection of piano onset more stable and robust. Transition-aware achieves a higher transcription F1 score than Onsets and Frames on MAESTRO dataset and MAPS dataset, reducing many extra note detection errors. This indicates that Transition-aware approach has better generalization ability on different datasets.

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This study investigates the potential of real-time inference of neural audio effects on Android smartphones, marking an initial step towards bridging the gap in neural audio processing for mobile devices. Focusing exclusively on processing rather than synthesis, we explore the performance of three open-source neural models across five Android phones released between 2014 and 2022, showcasing varied capabilities due to their generational differences. Through comparative analysis utilizing two C++ inference engines (ONNX Runtime and RTNeural), we aim to evaluate the computational efficiency and timing performance of these models, considering the varying computational loads and the hardware specifics of each device. Our work contributes insights into the feasibility of implementing neural audio processing in real-time on mobile platforms, highlighting challenges and opportunities for future advancements in this rapidly evolving field.

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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.

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This paper deals with the problem of beat-tracking in an audiofile. Considering time-variable tempo and meter estimation as input, we study two beat-tracking approaches. The first one is based on an adaptation of a method used in speech processing for locating the Glottal Closure Instants. The results obtained with this first approach allow us to derive a set of requirements for a robust approach. This second approach is based on a probabilistic framework. In this approach the beat-tracking problem is formulated as an “inverse” Viterbi decoding problem in which we decode times over beat-numbers according to observation and transition probabilities. A beat-template is used to derive the observation probabilities from the signal. For this task, we propose the use of a machine-learning method, the Linear Discriminant Analysis, to estimate the most discriminative beat-template. We finally propose a set of measures to evaluate the performances of a beattracking algorithm and perform a large-scale evaluation of the two approaches on four different test-sets.

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Information about the human spoken and singing voice is conveyed through the articulations of the individual’s vocal folds and vocal tract. The signal receiver, either human or machine, works at different levels of abstraction to extract and interpret only the relevant context specific information needed. Traditionally in the field of human machine interaction, the human voice is used to drive and control events that are discrete in terms of time and value. We propose to use the voice as a source of realvalued and time-continuous control signals that can be employed to interact with any multidimensional human-controllable device in real-time. The isolation of noise sources and the independence of the control dimensions play a central role. Their dependency on individual voice represents an additional challenge. In this paper we introduce a method to compute case specific independent signals from the vocal sound, together with an individual study of features computation and selection for noise rejection.

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In this paper we present graphical tools and parameters search algorithms for the timbre space exploration and design of complex sounds generated by physical modeling synthesis. The tools are built around a sparse representation of sounds based on Gammatone functions and provide the designer with both a graphical and an auditory insight. The auditory representation of a number of reference sounds, located as landmarks in a 2D sound design space, provides the designer with an effective aid to direct his search for new sounds. The sonic landmarks can either be synthetic sounds chosen by the user or be automatically derived by using clever parameter search and clustering algorithms. The proposed probabilistic method in this paper makes use of the sparse representations to model the distance between sparsely represented sounds. A subsequent optimization model minimizes those distances to estimate the optimal parameters, which generate the landmark sounds on the given auditory landscape.

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Since digital audio is encoded as discrete samples of the audio waveform, much can be said about a recording by the statistical properties of these samples. In this paper, a dataset of CD audio samples is analysed; the probability mass function of each audio clip informs a feature set which describes attributes of the musical recording related to loudness, dynamics and distortion. This allows musical recordings to be classified according to their “distortion character”, a concept which describes the nature of amplitude distortion in mastered audio. A subjective test was designed in which such recordings were rated according to the perception of their audio quality. It is shown that participants can discern between three different distortion characters; ratings of audio quality were significantly different (F (1, 2) = 5.72, p < 0.001, η 2 = 0.008) as were the words used to describe the attributes on which quality was assessed (χ2 (8, N = 547) = 33.28, p < 0.001). This expands upon previous work showing links between the effects of dynamic range compression and audio quality in musical recordings, by highlighting perceptual differences.

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