scholarly journals Feature Extraction of Music Signal Based on Adaptive Wave Equation Inversion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tianzhuo Gong ◽  
Sibing Sun
Keyword(s):  
Wave Equation ◽  
Objective Function ◽  
Wave Field ◽  
Phase Difference ◽  
Parameter Extraction ◽  
Decomposition Algorithm ◽  
Iteration Step ◽  
Time Frequency ◽  
Music Signal ◽  
Music Signals

The digitization, analysis, and processing technology of music signals are the core of digital music technology. There is generally a preprocessing process before the music signal processing. The preprocessing process usually includes antialiasing filtering, digitization, preemphasis, windowing, and framing. Songs in the popular wav format and MP3 format on the Internet are all songs that have been processed by digital technology and do not need to be digitalized. Preprocessing can affect the effectiveness and reliability of the feature parameter extraction of music signals. Since the music signal is a kind of voice signal, the processing of the voice is also applicable to the music signal. In the study of adaptive wave equation inversion, the traditional full-wave equation inversion uses the minimum mean square error between real data and simulated data as the objective function. The gradient direction is determined by the cross-correlation of the back propagation residual wave field and the forward simulation wave field with respect to the second derivative of time. When there is a big gap between the initial model and the formal model, the phenomenon of cycle jumping will inevitably appear. In this paper, adaptive wave equation inversion is used. This method adopts the idea of penalty function and introduces the Wiener filter to establish a dual objective function for the phase difference that appears in the inversion. This article discusses the calculation formulas of the accompanying source, gradient, and iteration step length and uses the conjugate gradient method to iteratively reduce the phase difference. In the test function group and the recorded music signal library, a large number of simulation experiments and comparative analysis of the music signal recognition experiment were performed on the extracted features, which verified the time-frequency analysis performance of the wave equation inversion and the improvement of the decomposition algorithm. The features extracted by the wave equation inversion have a higher recognition rate than the features extracted based on the standard decomposition algorithm, which verifies that the wave equation inversion has a better decomposition ability.

scholarly journals Graph-based feature extraction: A new proposal to study the classification of music signals outside the time-frequency domain

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0240915
Author(s):  
Dirceu de Freitas Piedade Melo ◽  
Inacio de Sousa Fadigas ◽  
Hernane Borges de Barros Pereira
Keyword(s):  
Feature Extraction ◽  
Fourier Transforms ◽  
Audio Signal ◽  
Rhythmic Activity ◽  
Attribute Selection ◽  
Time Frequency ◽  
Music Signal ◽  
Network Properties ◽  
Music Information ◽  
Music Signals

Most feature extraction algorithms for music audio signals use Fourier transforms to obtain coefficients that describe specific aspects of music information within the sound spectrum, such as the timbral texture, tonal texture and rhythmic activity. In this paper, we introduce a new method for extracting features related to the rhythmic activity of music signals using the topological properties of a graph constructed from an audio signal. We map the local standard deviation of a music signal to a visibility graph and calculate the modularity (Q), the number of communities (Nc), the average degree (〈k〉), and the density (Δ) of this graph. By applying this procedure to each signal in a database of various musical genres, we detected the existence of a hierarchy of rhythmic self-similarities between musical styles given by these four network properties. Using Q, Nc, 〈k〉 and Δ as input attributes in a classification experiment based on supervised artificial neural networks, we obtained an accuracy higher than or equal to the beat histogram in 70% of the musical genre pairs, using only four features from the networks. Finally, when performing the attribute selection test with Q, Nc, 〈k〉 and Δ, along with the main signal processing field descriptors, we found that the four network properties were among the top-ranking positions given by this test.

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

Music Onset Detection

2010 ◽  
pp. 297-316
Author(s):  
Ruohua Zhou ◽  
Josh D Reiss
Keyword(s):  
Performance Measures ◽  
Essential Role ◽  
General Scheme ◽  
Future Research ◽  
Onset Detection ◽  
Time Frequency ◽  
Detection Algorithms ◽  
Music Signal ◽  
Wide Range ◽  
Future Research Directions

Music onset detection plays an essential role in music signal processing and has a wide range of applications. This chapter provides a step by step introduction to the design of music onset detection algorithms. The general scheme and commonly-used time-frequency analysis for onset detection are introduced. Many methods are reviewed, and some typical energy-based, phase-based, pitch-based and supervised learning methods are described in detail. The commonly used performance measures, onset annotation software, public database and evaluation methods are introduced. The performance difference between energy-based and pitch-based method is discussed. The future research directions for music onset detection are also described.

Vertical seismic profile migration using the Kirchhoff integral

Geophysics ◽  
1988 ◽  
Vol 53 (6) ◽  
pp. 786-799 ◽  
Author(s):  
P. B. Dillon
Keyword(s):  
Wave Equation ◽  
Wave Field ◽  
Near Field ◽  
Synthetic Data ◽  
Real Data ◽  
Seismic Profile ◽  
Processing Strategies ◽  
Receiver Array ◽  
Vsp Data ◽  
Kirchhoff Integral

Wave‐equation migration can form an accurate image of the subsurface from suitable VSP data. The image’s extent and resolution are determined by the receiver array dimensions and the source location(s). Experiments with synthetic and real data show that the region of reliable image extent is defined by the specular “zone of illumination.” Migration is achieved through wave‐field extrapolation, subject to an imaging procedure. Wave‐field extrapolation is based upon the scalar wave equation and, for VSP data, is conveniently handled by the Kirchhoff integral. The migration of VSP data calls for imaging very close to the borehole, as well as imaging in the far field. This dual requirement is met by retaining the near‐field term of the integral. The complete integral solution is readily controlled by various weighting devices and processing strategies, whose worth is demonstrated on real and synthetic data.

Path-linking interpretation of Kirchhoff diffraction

1995 ◽  
Vol 450 (1938) ◽  
pp. 51-65 ◽  
Keyword(s):  
Exact Solution ◽  
Wave Equation ◽  
Wave Field ◽  
Diffraction Field ◽  
Scalar Wave ◽  
Wave Fields ◽  
Linking Number ◽  
Diffraction Integral ◽  
Pass Through ◽  
Kirchhoff Diffraction Integral

The Kirchhoff-diffraction integral is often used to describe the (scalar) wave field from a monochromatic point source in the presence of ‘opaque’ screens. Despite criticisms that can be made of its ‘derivation’, the Kirchhoff field is an exact solution of the wave equation, and exactly obeys definite, though unusual, boundary conditions (Kottler 1923, 1965). Here, the path-integral picture of wave fields is used to interpret the Kirchhoff-diffraction field in terms of all conceivable propagation paths, whether or not they pass through the opaque screens. Specifically, it is noted that the Kirchhoff field equals Ʃ(1 ─ m )ψ m , where the sum is over all integers m , and ψ m is the wave field due to all paths from the source to the field point for which the number of outward screen crossings minus the number of backwards screen crossings is m . Expressed more topologically, m is the total linking number of a path, when closed by any unobstructed path, with the screen edge lines. Other models of diffraction by screens are compared with Kirchhoff diffraction in the path interpretation.

scholarly journals Music Signal Separation Using Supervised Robust Non-Negative Matrix Factorization with β-divergence

2021 ◽  
Vol 15 ◽  
pp. 149-154
Author(s):  
Feng Li ◽  
Hao Chang
Keyword(s):  
Prior Knowledge ◽  
Matrix Factorization ◽  
Signal Separation ◽  
Separation Performance ◽  
Effective Strategy ◽  
Music Signal ◽  
Training Sequences ◽  
Conventional Methods ◽  
Music Signals ◽  
Non Negative Matrix Factorization

We propose a supervised method based on robust non-negative matrix factorization (RNMF) for music signal separation with β-divergence called supervised robust non-negative matrix factorization (SRNMF). Although RNMF method is an effective method for separating music signals, its separation performance degrades due to has no prior knowledge. To address this problem, in this paper, we develop SRNMF that unifying the robustness of RNMF and the prior knowledge to improve such separation performance on instrumental sound signals (e.g., piano, oboe and trombone). Application to the observed instrumental sound signals is an effective strategy by extracting the spectral bases of training sequences by using RNMF. In addition, β-divergence based on SRNMF be extended. The results obtained from our experiments on instrumental sound signals are promising for music signal separation. The proposed method achieves better separation performance than the conventional methods.

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