Advances in deep learning have led to novel, state-of-the-art techniques for blind source separation, particularly for the application of non-stationary noise removal from speech. In this paper, we show how a simple reformulation allows us to adapt blind source separation techniques to the problem of speech dereverberation and, accordingly, train a bidirectional recurrent neural network (BRNN) for this task. We compare the performance of the proposed neural network approach with that of a baseline dereverberation algorithm based on spectral subtraction. We find that our trained neural network quantitatively and qualitatively outperforms the baseline approach.

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This paper proposes a neural network for carrying out parametric equalizer (EQ) matching. The novelty of this neural network solution is that it can be optimized directly in the frequency domain by means of differentiable biquads, rather than relying solely on a loss on parameter values which does not correlate directly with the system output. We compare the performance of the proposed neural network approach with that of a baseline algorithm based on a convex relaxation of the problem. It is observed that the neural network can provide better matching than the baseline approach because it directly attempts to solve the non-convex problem. Moreover, we show that the same network trained with only a parameter loss is insufficient for the task, despite the fact that it matches underlying EQ parameters better than one trained with a combination of spectral and parameter losses.

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In this paper, we propose a new processor capable of directly changing the microdynamics of an audio signal primarily via a single dedicated user-facing parameter. The novelty of our processor is that it has built into it a measure of relative level, a short-term signal strength measurement which is robust to changes in signal macrodynamics. Consequent dynamic range processing is signal level-independent in its nature, and attempts to directly alter its observed relative level measurements. The inclusion of such a meter within our proposed processor also gives rise to a natural solution to the dynamics matching problem, where we attempt to transfer the microdynamic characteristics of one audio recording to another by means of estimating appropriate settings for the processor. We suggest a means of providing a reasonable initial guess for processor settings, followed by an efficient iterative algorithm to refine upon our estimates. Additionally, we implement the processor as a differentiable recurrent layer and show its effectiveness when wrapped around a gradient descent optimizer within a deep learning framework. Moreover, we illustrate that the proposed processor has more favorable gradient characteristics relative to a conventional dynamic range compressor. Throughout, we consider extensions of the processor, matching algorithm, and differentiable implementation for the multiband case.

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In this paper, we propose an approach to real-time singing voice conversion and outline its development as a plug-in suitable for streaming use in a digital audio workstation. In order to simultaneously ensure pitch preservation and reduce the computational complexity of the overall system, we adopt a source-filter methodology and consider a vocoder-free paradigm for modeling the conversion task. In this case, the source is extracted and altered using more traditional DSP techniques, while the filter is determined using a deep neural network. The latter can be trained in an end-toend fashion and additionally uses adversarial training to improve system fidelity. Careful design allows the system to scale naturally to sampling rates higher than the neural filter model sampling rate, outputting full-band signals while avoiding the need for resampling. Accordingly, the resulting system, when operating at 44.1 kHz, incurs under 60 ms of latency and operates 20 times faster than real-time on a standard laptop CPU.

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In this paper, we introduce means of improving fidelity and controllability of the RAVE generative audio model by factorizing pitch and other features. We accomplish this primarily by creating a multi-band excitation signal capturing pitch and/or loudness information, and by using it to FiLM-condition the RAVE generator. To further improve fidelity when applied to a singing voice application explored here, we also consider concatenating a supervised phonetic encoding to its latent representation. An ablation analysis highlights the improved performance of our incremental improvements relative to the baseline RAVE model. As our primary enhancement involves adding a stable pitch conditioning mechanism into the RAVE model, we simply call our method P-RAVE.

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