Friction fault diagnosis of rotating machinery based on acoustic emission (AE) technique is a research hotspot in recent years. The rotating machinery will produce multi-source noise during the operation process, so how to correctly identify the friction acoustic emission signals has become a key factor for accurate diagnosis of the fault. In this paper, it proposes a Gaussian mixed model (GMM)...

This paper presents a system for acoustic scene classification (SC) that is applied to data of the SC task of the DCASE’16 challenge (Task 1). The proposed method is based on extracting acoustic features that employ a relatively long temporal context, i.e., amplitude modulation filer bank (AMFB) features, prior to detection of acoustic scenes using a neural network (NN) based classification app...

In this paper we propose a new learning algorithm for locally recurrent neural networks, called Truncated Recursive Back Propagation which can be easily implemented on-line with good performance. Moreover it generalises the algorithm proposed by Waibel et al. for TDNN, and includes the Back and Tsoi algorithm as well as BPS and standard on-line Back Propagation as particular cases. The proposed...

Identifying a speaker’s native language with his speech in a second language is useful for many human-machine voice interface applications. In this paper, we use a sub-phone-based i-vector approach to identify non-native English speakers’ native languages by their English speech input. Time delay deep neural networks (TDNN) are trained on LVCSR corpora for improving the alignment of speech utte...

Phoneme classification and recognition is the first step to large vocabulary continuous speech recognition. This step represents the acoustic modeling part of such a system. In hybrid speech recognition systems phoneme recognition is made by artificial neural networks (ANN’s). The main objective of this paper is the investigation of dynamic ANN’s, namely the Time-Delay Neural Networks (TDNN) an...

This paper presents a new short-term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is synthesized using a Genetic Algorithm (GA). The model predicts flow and occupancy values at a given freeway section based on contributions from their recent temporal profile (over a few minutes) as well the spatial profile (including inputs f...

We present a Multi-State Time Del ay Neural Network (MS-TDNN) for speaker-i ndependent, connected l etter recogni ti on. Our MS-TDNNachi eves 98. 5/92.0% word accuracy on speaker dependent/i ndependent Engl i sh l etter tasks[7, 8]. In thi s paper we wi l l summari ze several techni ques to improve (a) conti nuous recogni ti on performance, such as sentence l evel trai ni ng, and (b) phoneti c ...

The automatic recognition of cursive Korean characters is a di$cult problem, not only due to the multiple possible variations involved in the shapes of characters, but also because of the interconnections of neighboring graphemes within an individual character. This paper proposes a recognition method for Korean characters using graph representation. This method uses a time-delay neural network...

In this paper, we explore the effectiveness of a variety of Deep Learning-based acoustic models for conversational telephony speech, specifically TDNN, bLSTM and CNN-bLSTM models. We evaluated these models on both research testsets, such as Switchboard and CallHome, as well as recordings from a realworld call-center application. Our best single system, consisting of a single CNN-bLSTM acoustic ...

We deene a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma lters (as proposed by de Vries and Principe (de Vries & Principe 1992)) and associated gain terms throughout all layers. We derive gradient descent update equations and apply the model to the recognition of speech phonemes. We nd that both the inclusion of gamma lters in all layers, and the...