Spectrogram

About: Spectrogram is a research topic. Over the lifetime, 5813 publications have been published within this topic receiving 81547 citations.

A procedure of vibration analysis from planetary gearbox under non-stationary cyclic operations by instantaneous frequency estimation in time-frequency domain

Abstract: Condition monitoring of gearboxes via vibration analysis is well-recognized approach in scientific literature and also in engineering practice. However, in many cases machine works under non-stationary operating conditions (load and speed variation), that often requires special signal processing and pattern recognition suitable for time varying systems. One of key problem is to identify variation of external load or speed. Measurement of current consumed by electric motor or instantaneous speed obtained by processing of tachometer signal, in many practical situations (industrial condition) may be difficult or impossible. In such case non-stationary load variation may be identified by extraction of information hidden in vibration signal. For example it may be extracted from amplitude or frequency demodulation. Unfortunately both approaches are difficult (or even impossible) for our machines due to complexity of design and wide range of load/speed variation. In order to avoid these constrains in this paper new approach will be proposed and exploited for complex, multistage gearboxes with planetary stage. To extract information related to cyclic load variation, an instantaneous speed obtained via time-frequency spectrogram will be used. Algorithms for Instantaneous Frequency (IF) estimation via T-F maps have been initially developed by Millioz and Martin. In this paper a novel procedure for Instantaneous Speed estimation (based on IF identification by mentioned automatic algorithm) will be proposed, next the procedure will be applied to vibration signals from planetary gearboxes.

Independent Low-Rank Tensor Analysis for Audio Source Separation

Abstract: This paper describes a versatile tensor factorization technique called independent low-rank tensor analysis (ILRTA) and its application to single-channel audio source separation. In general, audio source separation has been conducted in the short-time Fourier transform (STFT) domain under an unrealistic but conventional assumption of the independence of time-frequency (TF) bins. Nonnegative matrix factorization (NMF) is a typical technique of single-channel source separation based on the low-rankness of source spectrograms. In a multichannel setting, independent component analysis (ICA) and its multivariate extension called independent vector analysis (IVA) have often been used for blind source separation based on the independence of source spectrograms. Integrating NMF and IVA, independent low-rank matrix analysis (ILRMA) was recently proposed. To deal with the covariance of TF bins, in this paper we propose ILRTA as a new extension of NMF. Both ILRMA and ILRTA aim to find independent and low-rank sources. A key difference is that while ILRMA estimates demixing filters that decorrelate the channels for multichannel source separation, ILRTA finds optimal transforms that decorrelate the time frames and frequency bins of a STFT representation for single-channel source separation in a way that the bin-wise independence assumed by NMF holds true as much as possible. We report evaluation results of ILRTA and discuss extension of ILRTA to multichannel source separation.

Combining Visual and Acoustic Features for Bird Species Classification

Abstract: In this paper a novel approach for automatic bird species classification is described. The proposed strategy is based on features taken from the textural content of spectrogram images of bird vocalizations. We show how several texture descriptors can be used for representing the spectrograms. The following approaches are tested here with spectrograms for the first time: Local Ternary Phase Quantization, Heterogeneous Auto-Similarities of Characteristics, and an ensemble of variants of Local Binary Pattern Histogram Fourier. Combining this set of descriptors greatly increases classification performance and markedly improves previous ensembles of texture descriptors used for describing a spectrogram. Moreover, a further improvement is obtained when the texture descriptors are combined with the acoustic features. SVM classifiers are used in the classification step, with final results computed using 10-fold cross-validation. For a fair comparison with other methods in the literature, the experiments are performed on a benchmark database composed of 46 bird species used for this classification task. The best accuracy rate obtained is about 94.5%. The MATLAB code we used is publicly available to other researchers for future comparisons, as well as the database used in the experiments.

Blind estimation of bandwidth and duration parameters of an incoming signal

Abstract: A method and system for blind estimation of time and frequency parameters of an incoming signal. A spectrogram of the signal is processed using singular value decomposition (SVD) and ICA (independent component analysis) techniques. Time and frequency parameters are estimated from time and frequency eigenvectors, respectively.

SIFT-based local spectrogram image descriptor: a novel feature for robust music identification

Abstract: Music identification via audio fingerprinting has been an active research field in recent years. In the real-world environment, music queries are often deformed by various interferences which typically include signal distortions and time-frequency misalignments caused by time stretching, pitch shifting, etc. Therefore, robustness plays a crucial role in music identification technique. In this paper, we propose to use scale invariant feature transform (SIFT) local descriptors computed from a spectrogram image as sub-fingerprints for music identification. Experiments show that these sub-fingerprints exhibit strong robustness against serious time stretching and pitch shifting simultaneously. In addition, a locality sensitive hashing (LSH)-based nearest sub-fingerprint retrieval method and a matching determination mechanism are applied for robust sub-fingerprint matching, which makes the identification efficient and precise. Finally, as an auxiliary function, we demonstrate that by comparing the time-frequency locations of corresponding SIFT keypoints, the factor of time stretching and pitch shifting that music queries might have experienced can be accurately estimated.