Download Perceptual Linear Filters: Low-Order ARMA Approximation for Sound Synthesis This paper deals with the approximation of a given frequency response by a low-order linear ARMA filter (Auto-Regressive Moving Average). The aim of this work is the audio synthesis, then to improve the perceptual quality, a criterion based on human listening is defined and minimized. Two complementary approaches are proposed here for solving this non-linear and non-convex problem: first, a weighted version of the Iterative Prefiltering, second, an adaptation of the Gauss-Newton method. This algorithm is adapted to guarantee the causality/stability of the obtained filter, and eventually its minimum phase property. The benefit of the new method is illustrated and evaluated.
Download A Preliminary Model for the Synthesis of Source Spaciousness We present here a basic model for the synthesis of source spaciousness over loudspeaker arrays. This model is based on two experiments carried out to quantify the contribution of early reflections and reverberation to the perception of source spaciousness.
Download A Two Level Montage Approach to Sound Texture Synthesis with Treatment of Unique Events In this paper a novel algorithm for sound texture synthesis is presented. The goal of this algorithm is to produce new examples of a given sampled texture, the synthesized textures being of any desired duration. The algorithm is based on a montage approach to synthesis in that the synthesized texture is made up of pieces of the original sample concatenated together in a new sequence. This montage approach preserves both the high level evolution and low level detail of the original texture. Included in the algorithm is a measure of uniqueness, which can be used for the identification of regions in the original texture containing events that are atypical of the texture, and hence avoid their unnatural repetition at the synthesis stage.
Download A Pitch Salience Function Derived from Harmonic Frequency Deviations for Polyphonic Music Analysis In this paper, a novel approach for the computation of a pitch salience function is presented. The aim of a pitch (considered here as synonym for fundamental frequency) salience function is to estimate the relevance of the most salient musical pitches that are present in a certain audio excerpt. Such a function is used in numerous Music Information Retrieval (MIR) tasks such as pitch, multiple-pitch estimation, melody extraction and audio features computation (such as chroma or Pitch Class Profiles). In order to compute the salience of a pitch candidate f , the classical approach uses the weighted sum of the energy of the short time spectrum at its integer multiples frequencies hf . In the present work, we propose a different approach which does not rely on energy but only on frequency location. For this, we first estimate the peaks of the short time spectrum. From the frequency location of these peaks, we evaluate the likelihood that each peak is an harmonic of a given fundamental frequency. The specificity of our method is to use as likelihood the deviation of the harmonic frequency locations from the pitch locations of the equal tempered scale. This is used to create a theoretical sequence of deviations which is then compared to an observed one. The proposed method is then evaluated for a task of multiple-pitch estimation using the MAPS test-set.
Download Automatic Tablature Transcription of Electric Guitar Recordings by Estimation of Score- and Instrument-Related Parameters In this paper we present a novel algorithm for automatic analysis, transcription, and parameter extraction from isolated polyphonic guitar recordings. In addition to general score-related information such as note onset, duration, and pitch, instrumentspecific information such as the plucked string, the applied plucking and expression styles are retrieved automatically. For this purpose, we adapted several state-of-the-art approaches for onset and offset detection, multipitch estimation, string estimation, feature extraction, and multi-class classification. Furthermore we investigated a robust partial tracking algorithm with respect to inharmonicity, an extensive extraction of novel and known audio features as well as the exploitation of instrument-based knowledge in the form of plausability filtering to obtain more reliable prediction. Our system achieved very high accuracy values of 98 % for onset and offset detection as well as multipitch estimation. For the instrument-related parameters, the proposed algorithm also showed very good performance with accuracy values of 82 % for the string number, 93 % for the plucking style, and 83 % for the expression style. Index Terms - playing techniques, plucking style, expression style, multiple fundamental frequency estimation, string classification, fretboard position, fingering, electric guitar, inharmonicity coefficient, tablature
Download The Modulation Scale Spectrum and its Application to Rhythm-Content Description In this paper, we propose the Modulation Scale Spectrum as an extension of the Modulation Spectrum through the Scale domain. The Modulation Spectrum expresses the evolution over time of the amplitude content of various frequency bands by a second Fourier Transform. While its use has been proven for many applications, it is not scale-invariant. Because of this, we propose the use of the Scale Transform instead of the second Fourier Transform. The Scale Transform is a special case of the Mellin Transform. Among its properties is "scale-invariance". This implies that two timestretched version of a same music track will have (almost) the same Scale Spectrum. Our proposed Modulation Scale Spectrum therefore inherits from this property while describing frequency content evolution over time. We then propose a specific implementation of the Modulation Scale Spectrum in order to represent rhythm content. This representation is therefore tempo-independent. We evaluate the ability of this representation to catch rhythm characteristics on a classification task. We demonstrate that for this task our proposed representation largely exceeds results obtained so far while being highly tempo-independent.
Download Prioritized Computation for Numerical Sound Propagation The finite difference time domain (FDTD) method is commonly used as a numerically accurate way of propagating sound. However, it requires extensive computation. We present a simple method for accelerating FDTD. Specifically, we modify the FDTD update loop to prioritize computation where it is needed most in order to faithfully propagate waves through the simulated space. We estimate for each potential cell update its importance to the simulation output and only update the N most important cells, where N is dependent on the time available for computation. In this paper, we explain the algorithm and discuss how it can bring enhanced accuracy and dynamism to real-time audio propagation.
Download Polyphonic Pitch Detection by Iterative Analysis of the Autocorrelation Function In this paper, a polyphonic pitch detection approach is presented, which is based on the iterative analysis of the autocorrelation function. The idea of a two-channel front-end with periodicity estimation by using the autocorrelation is inspired by an algorithm from Tolonen and Karjalainen. However, the analysis of the periodicity in the summary autocorrelation function is enhanced with a more advanced iterative peak picking and pruning procedure. The proposed algorithm is compared to other systems in an evaluation with common data sets and yields good results in the range of state of the art systems.
Download Streaming Spectral Processing with Consumer-Level Graphics Processing Units This paper describes the implementation of a streaming spectral processing system for realtime audio in a consumer-level onboard GPU (Graphics Processing Unit) attached to an off-the-shelf laptop computer. It explores the implementation of four processes: standard phase vocoder analysis and synthesis, additive synthesis and the sliding phase vocoder. These were developed under the CUDA development environment as plugins for the Csound 6 audio programming language. Following a detailed exposition of the GPU code, results of performance tests are discussed for each algorithm. They demonstrate that such a system is capable of realtime audio, even under the restrictions imposed by a limited GPU capability.
Download Music-Content-Adaptive Robust Principal Component Analysis for a Semantically Consistent Separation of Foreground and Background in Music Audio Signals Robust Principal Component Analysis (RPCA) is a technique to decompose signals into sparse and low rank components, and has recently drawn the attention of the MIR field for the problem of separating leading vocals from accompaniment, with appealing results obtained on small excerpts of music. However, the performance of the method drops when processing entire music tracks. We present an adaptive formulation of RPCA that incorporates music content information to guide the decomposition. Experiments on a set of complete music tracks of various genres show that the proposed algorithm is able to better process entire pieces of music that may exhibit large variations in the music content, and compares favorably with the state-of-the-art.