ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.
- Read more about Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation
- Log in to post comments
In this paper, we present one saliency-based feature selection and tracking strategy in the feature-based stereoscopic panoramic video generation system. Many existing stereoscopic video composition approaches aims at producing high-quality panoramas from multiple input cameras; however, most of them directly operate image alignment on those originally detected features without any refinement or optimization. The standard global feature extraction threshold always fails to guarantee stitching correctness of all human interested regions.
- Categories:
We extend the classical joint problem of signal demixing, blind deconvolution,
and filter identification to the realm of graphs. The model is that
each mixing signal is generated by a sparse input diffused via a graph filter.
Then, the sum of diffused signals is observed. We identify and address
two problems: 1) each sparse input is diffused in a different graph; and 2)
all signals are diffused in the same graph. These tasks amount to finding
the collections of sources and filter coefficients producing the observation.
- Categories:
- Read more about An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings
- Log in to post comments
- Categories:
- Read more about Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
- Log in to post comments
Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by fine-tuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective metrics in two very different languages: Catalan and Korean.
- Categories:
- Read more about MULTILINGUAL SPEECH RECOGNITION WITH A SINGLE END-TO-END MODEL
- Log in to post comments
Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the subword unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their
- Categories:
- Read more about Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
- Log in to post comments
Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by fine-tuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective metrics in two very different languages: Catalan and Korean.
- Categories:
Multi-image alignment, bringing a group of images into common register, is an ubiquitous problem and the first step of many applications in a wide variety of domains. As a result, a great amount of effort is being invested in developing efficient multi-image alignment algorithms. Little has been done, however, to answer fundamental practical questions such as: what is the comparative performance of existing methods? is there still room for improvement? under which conditions should one technique be preferred over another?
- Categories:
This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discovering a global model, the individual agents are sensing data that is only dependent on parts of the model. Moreover, different agents may be dependent on different subsets of the model. In this way, cooperation is justified and also necessary to enable recovery of the global information.
- Categories:
- Read more about PARALLEL BEAMFORMING DESIGN IN FULL DUPLEX SYSTEMS WITH PER-ANTENNA POWER CONSTRAINTS
- Log in to post comments
We investigate the max-min weighted downlink signal-to-interference ratio (SINR) problem under uplink SINR
constraints and practical per-antenna constraints in full-duplex systems. The successive convex approximation (SCA)
- Categories:
- Read more about HUMAN-MACHINE INFERENCE NETWORKS FOR SMART DECISION MAKING: OPPORTUNITIES AND CHALLENGES
- Log in to post comments
The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions.
- Categories: