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

Validation Optimisation using Machine Learning Techniques

Integration and validation is the most vital part before releasing products to customers in Intel. The validation team qualifies the release based on multiple stages of validation on hardware and software stack. Bugs are raised after execution of test cases on each platform and so similar bugs arise which are filed by the user. There is a immediate concern on this and hence, many issues are closed as duplicates.The main objective is to find these similar bugs for each bug filed and thereby,debug efforts can be reused.Similar bugs are found by term based search using ElasticSearch ,a text search engine and neural network based search where context is considered.Using elasticsearch,scoring algorithms based on driver  versions and platform hierarchy are applied to rank the similar bugs. LSTM neural networks are also incorporated to predict duplicate bugs by considering context of the sentence and thereby, increasing accuracy