Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Improving Automatic Speech Recognition on Endangered Languages

As the world moves towards a more globalized scenario, it has brought along with it the extinction of several languages. It has been estimated that over the next century, over half of the world\u27s languages will be extinct, and an alarming 43% of the world\u27s languages are at different levels of endangerment or extinction already. The survival of many of these languages depends on the pressure imposed on the dwindling speakers of these languages. Often there is a strong correlation between endangered languages and the number and quality of recordings and documentations of each. But why do we care about preserving these less prevalent languages? The behavior of cultures is often expressed in the form of speech via one\u27s native language. The memories, ideas, major events, practices, cultures and lessons learnt, both individual as well as the community\u27s, are all communicated to the outside world via language. So, language preservation is crucial to understanding the behavior of these communities. Deep learning models have been shown to dramatically improve speech recognition accuracy but require large amounts of labelled data. Unfortunately, resource constrained languages typically fall short of the necessary data for successful training. To help alleviate the problem, data augmentation techniques fabricate many new samples from each sample. The aim of this master\u27s thesis is to examine the effect of different augmentation techniques on speech recognition of resource constrained languages. The augmentation methods being experimented with are noise augmentation, pitch augmentation, speed augmentation as well as voice transformation augmentation using Generative Adversarial Networks (GANs). This thesis also examines the effectiveness of GANs in voice transformation and its limitations. The information gained from this study will further augment the collection of data, specifically, in understanding the conditions required for the data to be collected in, so that GANs can effectively perform voice transformation. Training of the original data on the Deep Speech model resulted in 95.03% WER. Training the Seneca data on a Deep Speech model that was pretrained on an English dataset, reduced the WER to 70.43%. On adding 15 augmented samples per sample, the WER reduced to 68.33%. Finally, adding 25 augmented samples per sample, the WER reduced to 48.23%. Experiments to find the best augmentation method among noise addition, pitch variation, speed variation augmentation and GAN augmentation revealed that GAN augmentation performed the best, with a WER reduction to 60.03%

Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems

Mobile and embedded devices are increasingly using microphones and audio-based computational models to infer user context. A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world. Besides many environmental dynamics, a primary factor that impacts the robustness of audio models is microphone variability. In this work, we propose Mic2Mic -- a machine-learned system component -- which resides in the inference pipeline of audio models and at real-time reduces the variability in audio data caused by microphone-specific factors. Two key considerations for the design of Mic2Mic were: a) to decouple the problem of microphone variability from the audio task, and b) put a minimal burden on end-users to provide training data. With these in mind, we apply the principles of cycle-consistent generative adversarial networks (CycleGANs) to learn Mic2Mic using unlabeled and unpaired data collected from different microphones. Our experiments show that Mic2Mic can recover between 66% to 89% of the accuracy lost due to microphone variability for two common audio tasks.Comment: Published at ACM IPSN 201