In this paper, a feature extraction method is proposed based on the non-negative matrix factorization (NMF) for classifiers for monitoring domestic activities with acoustic signals. Most of the classifiers of the acoustic signals use data-independent spectral features (e.g., log-Mel spectrum and Mel-frequency cepstral coefficients). Recently, some novel feature extraction methods have been researched, including convolution-NMF-based features combined with K-means clustering. This study proposes an enhanced NMF-based feature extraction method that is inspired by the NMF-based noise reduction algorithm. The proposed method independently estimates the frequency basis matrix for each class, and then cascades the basis matrices to form the entire frequency bases, where the acoustic signal is transformed to the proposed feature by estimating the temporal basis matrix with the trained frequency bases. In addition, this study proposes a data augmentation method for the proposed feature that is inspired by the “mix and shuffle” method for audio waveforms. In order to evaluate the proposed system, which consists of the proposed NMF-based feature and the convolutional-neural-network-based classifier, some evaluations were performed using the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Task 5 – Monitoring of Domestic Activities Based on Multi-channel Acoustics – Database. The results showed that the proposed system has comparable performance to that of state-of-the-art algorithms and that it has enhanced the F1-score performance of 6%–12% in comparison with the conventional NMF-based feature extraction method that is based on convolutional NMF and K-means clustering.

In this study, we propose a reverberation suppression algorithm for linear frequency-modulated (LFM) pulse sonar systems using a non-negative matrix factorization (NMF) method. Because conventional NMF-based reverberation suppression algorithms are only applicable to continuous wave reverberation, we propose two pre-processing methods, namely dechirping transformation and modulo operation, to facilitate application of the NMF method to LFM reverberation. Moreover, we impose additional sparse constraints on the NMF method to improve its performance. To evaluate the proposed algorithm, an experiment involving simulated LFM reverberation is performed. The results thereof show improved detection performance at several signal-to-reverberation ratios and false alarm conditions. Moreover, the proposed algorithm is also applied to sea experiment data. According to the sea experiment analysis, the algorithm is able to suppress the LFM reverberation effectively and improve detection performance in practical LFM pulse sonar systems.

In this paper, methods to estimate the number of basis vectors of the nonnegative matrix factorization (NMF) of automatic music transcription (AMT) systems are proposed. Previously, studies on NMF-based AMT have demonstrated that the number of basis vectors affects the performance and that the number of note events can be a good selection as the rank of NMF. However, many NMF-based AMT methods do not provide a method to estimate the appropriate number of basis vectors; instead, the number is assumed to be given in advance, even though the number of basis vectors significantly affects the algorithm’s performance. Recently, based on Bayesian methods, certain estimation algorithms for the number of basis vectors have been proposed; however, they are not designed to be used as music transcription algorithms but are components of specific NMF methods and thus cannot be used generally as NMF-based transcription algorithms. Our proposed estimation algorithms are based on eigenvalue decomposition and Stein’s unbiased risk estimator (SURE). Because the SURE method requires variance in undesired components as a priori knowledge, the proposed algorithms estimate the value using random matrix theory and first and second onset information in the input music signal. Experiments were then conducted for the AMT task using the MIDI-aligned piano sounds (MAPS) database, and these algorithms were compared with variational NMF, gamma process NMF, and NMF with automatic relevance determination algorithms. Based on experimental results, the conventional NMF-based transcription algorithm with the proposed rank estimation algorithms demonstrated enhanced F1 score performances of 2–3% compared to the algorithms. While the performance advantages are not significantly large, the results are meaningful because the proposed algorithms are lightweight, are easy to combine with any other NMF methods that require an a priori rank parameter, and do not have setting parameters that considerably affect the performance.

In active sonar systems, detection always suffers from reverberation interference from multiple scatterers in oceanic environments; therefore, numerous studies have been conducted on reverberation suppression. Recently, a non-negative matrix factorization (NMF)-based method was proposed and successfully applied to reverberation suppression. However, the conventional NMF-based method makes convergence challenging because the frequency basis matrix is initialized without considering reverberation characteristic information from oceanic environments. To solve these problems, We propose an improved NMF-based reverberation suppression method adopting a pre-trained reverberation basis matrix and modified sparse update rule. The proposed method is evaluated by analyzing simulation and sea experiment data and the study confirmed that the detection performance was improved compared to the conventional method under various signal-to-reverberation ratio conditions. Several topics are also discussed to analyze the proposed method in detail.

In this paper, feature extraction methods are developed based on the non-negative matrix factorization (NMF) algorithm to be applied in weakly supervised sound event detection. Recently, the development of various features and systems have been attempted to tackle the problems of acoustic scene classification and sound event detection. However, most of these systems use data-independent spectral features, e.g., Mel-spectrogram, log-Mel-spectrum, and gammatone filterbank. Some data-dependent feature extraction methods, including the NMF-based methods, recently demonstrated the potential to tackle the problems mentioned above for long-term acoustic signals. In this paper, we further develop the recently proposed NMF-based feature extraction method to enable its application in weakly supervised sound event detection. To achieve this goal, we develop a strategy for training the frequency basis matrix using a heterogeneous database consisting of strongly- and weakly-labeled data. Moreover, we develop a non-iterative version of the NMF-based feature extraction method so that the proposed feature extraction method can be applied as a part of the model structure similar to the modern “on-the-fly” transform method for the Mel-spectrogram. To detect the sound events, the temporal basis is calculated using the NMF method and then used as a feature for the mean-teacher-model-based classifier. The results are improved for the event-wise post-processing method. To evaluate the proposed system, simulations of the weakly supervised sound event detection were conducted using the Detection and Classification of Acoustic Scenes and Events 2020 Task 4 database. The results reveal that the proposed system has F1-score performance comparable with the Mel-spectrogram and gammatonegram and exhibits 3–5% better performance than the log-Mel-spectrum and constant-Q transform.

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with $l_1$-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.