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ICASSP 2021 - IEEE International Conference on Acoustics, Speech and Signal Processing is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2021 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.
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- Read more about Riemannian Geometry-Based Decoding of the Directional Focus of Auditory Attention Using EEG
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- Read more about UNSUPERVISED MUSICAL TIMBRE TRANSFER FOR NOTIFICATION SOUNDS
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We present a method to transform artificial notification sounds into various musical timbres. To tackle the issues of ambiguous timbre definition, the lack of paired notification-music sample sets, and the lack of sufficient training data of notifications, we adapt the problem for a cycle-consistent generative adversarial network and train it with unpaired samples from the source and the target domains. In addition, instead of training the network with notification sound samples, we train it with video game music samples that share similar timbral features.
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- Read more about Modurec: Recommender Systems with Feature and Time Modulation
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Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which can help solve two well identified problems of collaborative filtering: cold start (not enough data is available for new users or products) and concept shift (the distribution of ratings changes over time).
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- Read more about Modurec: Recommender Systems with Feature and Time Modulation
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Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which can help solve two well identified problems of collaborative filtering: cold start (not enough data is available for new users or products) and concept shift (the distribution of ratings changes over time).
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- Read more about Differential Chaos Shift Keying-based Wireless Power Transfer
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In this work, we investigate differential chaos shift keying (DCSK), a communication-based waveform, in the context of wireless power transfer (WPT). Particularly, we present a DCSK-based WPT architecture, that employs an analog correlator at the receiver in order to boost the energy harvesting (EH) performance. By taking into account the nonlinearities of the EH process, we derive closed-form analytical expressions for the peak-to-average-power-ratio of the received signal as well as the harvested power.
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- Read more about Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks
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poster_landscape.pdf
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- Read more about Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks
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slides.pdf
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- Read more about Multi-Vehicle Velocity Estimation Using IEEE 802.11ad Waveform
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Wireless communication systems are to use millimeter-wave (mmWave) spectra, which can enable extra radar functionalities. In this paper, we propose a multi-target velocity estimation technique using IEEE 802.11ad waveform in a vehicle-to-vehicle (V2V) scenario. We form a wide beam to consider multiple target vehicles.
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- Read more about Resolution Limits of 20 Questions Search Strategies for Moving Targets
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We establish fundamental limits of tracking a moving target over the unit cube under the framework of 20 questions with measurement-dependent noise. In this problem, there is an oracle who knows the instantaneous location of a target. Our task is to query the oracle as few times as possible to accurately estimate the trajectory of the moving target, whose initial location and velocity is unknown. We study the case where the oracle's answer to each query is corrupted by random noise with query-dependent discrete distribution.
poster_moving2.pdf
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