- Transducers
- Spatial and Multichannel Audio
- Source Separation and Signal Enhancement
- Room Acoustics and Acoustic System Modeling
- Network Audio
- Audio for Multimedia
- Audio Processing Systems
- Audio Coding
- Audio Analysis and Synthesis
- Active Noise Control
- Auditory Modeling and Hearing Aids
- Bioacoustics and Medical Acoustics
- Music Signal Processing
- Loudspeaker and Microphone Array Signal Processing
- Echo Cancellation
- Content-Based Audio Processing
- Read more about Study Of Dense Network Approaches For Speech Emotion Recognition
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- Read more about Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment
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- Read more about Compressive Sampling of Sound Fields Using Moving Microphones
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- Read more about MULTI-DIALECT SPEECH RECOGNITION WITH A SINGLE SEQUENCE-TO-SEQUENCE MODEL
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- Read more about MULTI-SCALE OBJECT DETECTION WITH FEATURE FUSION AND REGION OBJECTNESS NETWORK
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- Read more about Whole Sentence Neural Language Model
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Recurrent neural networks have become increasingly popular for the task of language modeling achieving impressive gains in state-of-the-art speech recognition and natural language processing (NLP) tasks. Recurrent models exploit word dependencies over a much longer context window (as retained by the history states) than what is feasible with n-gram language models.
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- Read more about Signboard Saliency Detection in Street Videos
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- Read more about Acoustic Reflector Localization and Classification
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The process of understanding acoustic properties of environments is important for several applications, such as spatial audio, augmented reality and source separation. In this paper, multichannel room impulse responses are recorded and transformed into their direction of arrival (DOA)-time domain, by employing a superdirective beamformer. This domain can be represented as a 2D image. Hence, a novel image processing method is proposed to analyze the DOA-time domain, and estimate the reflection times of arrival and DOAs. The main acoustically reflective objects are then localized.
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- Read more about CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS
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Coral species, with complex morphology and ambiguous boundaries, pose a great challenge for automated classification. CNN activations, which are extracted from fully connected layers of deep networks (FC features), have been successfully used as powerful universal representations in many visual tasks. In this paper, we investigate the transferability and combined performance of FC features and CONV features (extracted
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- Read more about Determined Blind Source Separation via Proximal Splitting Algorithm
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The state-of-the-art algorithms of determined blind source separation (BSS) methods based on the independent component analysis
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