Unsupervised Learning of Semantic Audio Representations

Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change the underlying sound category, (ii) a mixture of two sound events inherits the categories of the constituents, and (iii) the categories of events in close temporal proximity are likely to be the same or related. Without labels to ground them, these constraints are incompatible with classification loss functions. However, they may still be leveraged to identify geometric inequalities needed for triplet loss-based training of convolutional neural networks. The result is low-dimensional embeddings of the input spectrograms that recover 41% and 84% of the performance of their fully-supervised counterparts when applied to downstream query-by-example sound retrieval and sound event classification tasks, respectively. Moreover, in limited-supervision settings, our unsupervised embeddings double the state-of-the-art classification performance.Comment: Submitted to ICASSP 201

CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

Verifying Memory System Protocols

We have proposed a framework for verifying that multiprocessor memory systems satisfy the requirements of memory consistency models. As an increasing number of optimizations and relaxed consistency models are being used in modern multiprocessors, a methodology for proving system correctness is necessary to convince memory system designers that their systems behave correctly. The verification framework utilizes a logical clocking scheme to define a total ordering on the events occurring in the system. We then prove properties of this ordering that guarantee the satisfaction of a particular memory consistency model. In this report, we provide proofs that show that two simple memory systems (a bus-based system and a directory-based system) observe sequential consistency. We also outline the ways in which this method could be applied to prove that more aggressive memory systems observe more relaxed consistency models. 1 Introduction Memory systems for parallel computers are becoming incre..

Lamport Clocks: Verifying a Directory Cache-Coherence Protocol

Modern shared-memory multiprocessors use complex memory system implementations that include a variety of non-trivial and interacting optimizations. More time is spent in verl$ving the correctness of such implementations than in designing the system. In particular; large-scale Distributed Shared Memory (DSM) systems usually rely on a directory cache-coherence protocol to provide the illusion of a sequentially consistent shared address space. Verifying that such a distributed protocol satisfies sequential consistency is a dificult task. Current formal protocol verification techniques [18] complement simulation, but are somewhat nonintuitive to system designers and verl$ers, and they do not scale well to practical systems. In this papes we examine a new reasoning technique that is precise and (we find) intuitive. Our technique is based on Lamport’s logical clocks, which were originally used in distributed systems. We make modest extensions to Lamport’s logical clocking scheme to assign timestamps to relevant protocol events to construct a total ordering of such events. Such total orderings can be used to verify that the requirements of a particular memory consistency model have been satisjed. We apply Lamport clocks to prove that a non-trivial directory protocol implements sequential consistency. To do this, we describe an SC1 Origin 2000~like protocol [12] in detail, provide a timestamping scheme that totally orders all protocol events, and then prove sequential consistency (i.e., a load always returns the value of the “last ” store to the same address in timestamp order).