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Register a new userDifferentiable Signal Processing With Black-Box Audio Effects
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We present a data-driven approach to automate audio signal processing by incorporating stateful third-party, audio effects as layers within a deep neural network. We then train a deep encoder to analyze input audio and control effect parameters to perform the desired signal manipulation, requiring only input-target paired audio data as supervision. To train our network with non-differentiable black-box effects layers, we use a fast, parallel stochastic gradient approximation scheme within a standard auto differentiation graph, yielding efficient end-to-end backpropagation. We demonstrate the power of our approach with three separate automatic audio production applications: tube amplifier emulation, automatic removal of breaths and pops from voice recordings, and automatic music mastering. We validate our results with a subjective listening test, showing our approach not only can enable new automatic audio effects tasks, but can yield results comparable to a specialized, state-of-the-art commercial solution for music mastering.
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Most audio processing pipelines involve transformations that act on fixed-dimensional input representations of audio. For example, when using the Short Time Fourier Transform (STFT) the DFT size specifies a fixed dimension for the input representation. As a consequence, most audio machine learning models are designed to process fixed-size vector inputs which often prohibits the repurposing of learned models on audio with different sampling rates or alternative representations. We note, however, that the intrinsic spectral information in the audio signal is invariant to the choice of the input representation or the sampling rate. Motivated by this, we introduce a novel way of processing audio signals by treating them as a collection of points in feature space, and we use point cloud machine learning models that give us invariance to the choice of representation parameters, such as DFT size or the sampling rate. Additionally, we observe that these methods result in smaller models, and allow us to significantly subsample the input representation with minimal effects to a trained model performance.
In this study, we present a deep learning-based speech signal-processing mobile application, called CITISEN, which can perform three functions: speech enhancement (SE), model adaptation (MA), and acoustic scene conversion (ASC). For SE, CITISEN can effectively reduce noise components from speech signals and accordingly enhance their clarity and intelligibility. When it encounters noisy utterances with unknown speakers or noise types, the MA function allows CITISEN to effectively improve the SE performance by adapting an SE model with a few audio files. Finally, for ASC, CITISEN can convert the current background sound into a different background sound. The experimental results confirmed the effectiveness of performing SE, MA, and ASC functions via objective evaluation and subjective listening tests. Moreover, the MA experimental results indicated that short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) could be improved by approximately 5% and 10%, respectively. The promising results reveal that the developed CITISEN mobile application can be potentially used as a front-end processor for various speech-related services such as voice communication, assistive hearing devices, and virtual reality headsets. In addition, CITISEN can be used as a platform for using and evaluating the newly performed deep-learning-SE models, and can flexibly extend the models to address various noise environments and users.
Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation consisting of log magnitude and instantaneous frequency (the IFSpectrogram). Many other synthesis systems use representations derived from the magnitude spectra, and then depend on a backend component to invert the output magnitude spectrograms that generally result in audible artefacts associated with the inversion process. However, for signals that have closely-spaced frequency components such as non-pitched and other noisy sounds, training the GAN on the 2-channel IFSpectrogram representation offers no advantage over the magnitude spectra based representations. In this paper, we propose that training GANs on single-channel magnitude spectra, and using the Phase Gradient Heap Integration (PGHI) inversion algorithm is a better comprehensive approach for audio synthesis modeling of diverse signals that include pitched, non-pitched, and dynamically complex sounds. We show that this method produces higher-quality output for wideband and noisy sounds, such as pops and chirps, compared to using the IFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable to using the IFSpectrogram, even while using a simpler representation with half the memory requirements.
Applications of deep learning to automatic multitrack mixing are largely unexplored. This is partly due to the limited available data, coupled with the fact that such data is relatively unstructured and variable. To address these challenges, we propose a domain-inspired model with a strong inductive bias for the mixing task. We achieve this with the application of pre-trained sub-networks and weight sharing, as well as with a sum/difference stereo loss function. The proposed model can be trained with a limited number of examples, is permutation invariant with respect to the input ordering, and places no limit on the number of input sources. Furthermore, it produces human-readable mixing parameters, allowing users to manually adjust or refine the generated mix. Results from a perceptual evaluation involving audio engineers indicate that our approach generates mixes that outperform baseline approaches. To the best of our knowledge, this work demonstrates the first approach in learning multitrack mixing conventions from real-world data at the waveform level, without knowledge of the underlying mixing parameters.
Audio captioning aims to automatically generate a natural language description of an audio clip. Most captioning models follow an encoder-decoder architecture, where the decoder predicts words based on the audio features extracted by the encoder. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often used as the audio encoder. However, CNNs can be limited in modelling temporal relationships among the time frames in an audio signal, while RNNs can be limited in modelling the long-range dependencies among the time frames. In this paper, we propose an Audio Captioning Transformer (ACT), which is a full Transformer network based on an encoder-decoder architecture and is totally convolution-free. The proposed method has a better ability to model the global information within an audio signal as well as capture temporal relationships between audio events. We evaluate our model on AudioCaps, which is the largest audio captioning dataset publicly available. Our model shows competitive performance compared to other state-of-the-art approaches.
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