In this paper, we propose an efficient and reproducible deep learning model for musical onset detection (MOD). We first review the state-of-the-art deep learning models for MOD, and identify their shortcomings and challenges: (i) the lack of hyper-parameter tuning details, (ii) the non-availability of code for training models on other datasets, and (iii) ignoring the network capability when comparing different architectures. Taking the above issues into account, we experiment with seven deep learning architectures. The most efficient one achieves equivalent performance to our implementation of the state-of-the-art architecture. However, it has only 28.3 of the total number of trainable parameters compared to the state-of-the-art. Our experiments are conducted using two different datasets: one mainly consists of instrumental music excerpts, and another developed by ourselves includes only solo singing voice excerpts. Further, inter-dataset transfer learning experiments are conducted. The results show that the model pre-trained on one dataset fails to detect onsets on another dataset, which denotes the importance of providing the implementation code to enable re-training the model for a different dataset. Datasets, code and a Jupyter notebook running on Google Colab are publicly available to make this research understandable and easy to reproduce.
Various audio input representations have been used for the first step of the pipeline, such as filtered logarithmic magnitude and phase spectrum @cite_15 @cite_13 . The former can be subdivided by the filterbank type -- Bark scale bands @cite_7 , Mel scale bands @cite_26 @cite_20 or constant-Q bands @cite_22 @cite_12 .
@cite_26 @cite_22 @cite_7 @cite_15 @cite_13 @cite_12 @cite_20
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Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end. Therefore, we investigate end-to-end source separation in the time-domain, which allows modelling phase information and avoids fixed spectral transformations. Due to high sampling rates for audio, employing a long temporal input context on the sample level is difficult, but required for high quality separation results because of long-range temporal correlations. In this context, we propose the Wave-U-Net, an adaptation of the U-Net to the one-dimensional time domain, which repeatedly resamples feature maps to compute and combine features at different time scales. We introduce further architectural improvements, including an output layer that enforces source additivity, an upsampling technique and a context-aware prediction framework to reduce output artifacts. Experiments for singing voice separation indicate that our architecture yields a performance comparable to a state-of-the-art spectrogram-based U-Net architecture, given the same data. Finally, we reveal a problem with outliers in the currently used SDR evaluation metrics and suggest reporting rank-based statistics to alleviate this problem. A 100 MS s pipelined ADC is digitally calibrated by a slow SigmaDelta ADC using a least-mean-square (LMS) algorithm. Both linear and nonlinear memoryless residue gain errors of the pipeline stages are adaptively corrected. With a 411 kHz sinusoidal input, the peak SNDR improves from 28 dB to 59 dB and the SFDR improves from 29 dB to 68 dB. The complete 0.13 mu ADC SoC occupies a die size of 3.7 mm times 4.7 mm, and consumes a total power of 448 mW. A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, is presented and examined. This technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical‐band spectral resolution, (2) the equal‐loudness curve, and (3) the intensity‐loudness power law. The auditory spectrum is then approximated by an autoregressive all‐pole model. A 5th‐order all‐pole model is effective in suppressing speaker‐dependent details of the auditory spectrum. In comparison with conventional linear predictive (LP) analysis, PLP analysis is more consistent with human hearing. The effective second formant F2’ and the 3.5‐Bark spectral‐peak integration theories of vowel perception are well accounted for. PLP analysis is computationally efficient and yields a low‐dimensional representation of speech. These properties are found to be useful in speaker‐independent automatic‐speech recognition. An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time-varying gain. Each sound source, in turn, is modeled as a sum of one or more components. The parameters of the components are estimated by minimizing the reconstruction error between the input spectrogram and the model, while restricting the component spectrograms to be nonnegative and favoring components whose gains are slowly varying and sparse. Temporal continuity is favored by using a cost term which is the sum of squared differences between the gains in adjacent frames, and sparseness is favored by penalizing nonzero gains. The proposed iterative estimation algorithm is initialized with random values, and the gains and the spectra are then alternatively updated using multiplicative update rules until the values converge. Simulation experiments were carried out using generated mixtures of pitched musical instrument samples and drum sounds. The performance of the proposed method was compared with independent subspace analysis and basic nonnegative matrix factorization, which are based on the same linear model. According to these simulations, the proposed method enables a better separation quality than the previous algorithms. Especially, the temporal continuity criterion improved the detection of pitched musical sounds. The sparseness criterion did not produce significant improvements