Note onset detection based on sparse decomposition

Abstract

Music onset detection is significant and essential for obtaining the high-level music features such as rhythm, beat, music paragraph and structure. The traditional methods for onset detection which employ Short Time Fourier Transform (STFT)-based or Wavelet Transform (WT)-based features to characterize music signal generally lack adaptiveness for representing the stationary and non-stationary part of the music signal. This will lead to the degraded performance for music note onset detection. To solve this problem, a new algorithm for note onset detection based on sparse decomposition is proposed. Firstly, the musical signals are sparsely decomposed with Matching Pursuit (MP), and then the hybrid detection algorithm which combines namely the Degree of Explanation (DE) and the Change of Partials (CP) is applied to the sparse representation of the music signal. Finally, a modified peak-picking algorithm is employed to generate onset vectors. The experiments on the dataset with 2050 onsets show that our results are superior to those of MIREX 2013. For the polyphonic music which is the most widely used form in our real life, the proposed algorithm has better performance than the other algorithms.

Combining DCQGMP-Based Sparse Decomposition and MPDR Beamformer for Multi-Type Interferences Mitigation for GNSS Receivers

“…Since in MP-based sparse decomposition, most of the calculation is spent on computing the inner products of the signal to be decomposed and the atoms [21], we can adopt the number of inner products to approximately express the computational complexities of the conventional MP and the proposed DCQGMP. To ensure the performance of interferences suppression, the interval of $f_{r_i}$, $T_{r_i}$, $t_{r_i}$ and $\phi$ should not be more than 10 Hz, 10 ns, 10 ns and $0.01\pi$, respectively.…”

“…The MP algorithm, dealing with the signal sparse decomposition effectively, is an iterative “greedy” algorithm [ 21 ]. Compared with other sparse decomposition algorithms, it has flexibility and adaptability to signals.…”

“…To address this problem, we introduce the signal sparse decomposition theory [ 20 ] into array signal processing. The sparse decomposition theory is gaining significant attention due to its advantages in digital signal processing and has been successfully applied to many fields, for example signal detection [ 21 ], clutter and jamming suppression [ 22 ]. Then, a cascaded multi-type interferences mitigation method combining improved DCQGMP-based sparse decomposition and the MPDR beamformer is proposed in this paper.…”

A Novel Tempogram Generating Algorithm Based on Matching Pursuit

“…To obtain the novelty curve, the musical signal is preprocessed firstly by separating the signal into multiple frequency bands, transient/steady state separation, etc. [14], and then predefined signal features, such as temporal features and spectral features [15][16][17], are extracted. Probabilistic methods [18][19][20] can also be employed to generate the novelty curve.…”

Cascaded Multitype Interferences Suppression Method Using Sparse Representation and Array Processing for GNSS Receiver

“…In this paper, a cascaded multitype interferences suppression method using sparse representation and array processing for GNSS receivers is proposed. Firstly, the signal sparse decomposition theory [14] is introduced into array signal processing, which is gaining significant attention and has been successfully applied to many fields, such as clutter and jamming suppression for airborne radar [15] and signal detection [16]. A novel overcomplete dictionary composed of linear frequency modulation atoms is designed.…”