ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2016 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics.
- Read more about LOW-COMPLEXITY BEAMFORMING DESIGNS OF SUM SECRECY RATE MAXIMIZATION FOR THE GAUSSIAN MISO MULTI-RECEIVER WIRETAP CHANNEL
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Poster for the paper 'Low-Complexity Beamforming Designs of Sum Secrecy Rate Maximization for the Gaussian MISO Multi-Receiver Wiretap Channel'.
Tang Yanqun's interests are in communication theory and signal processing, including MIMO systems, physical layer security, simultaneous wireless information and power transfer, visual light communications.
Email: tangyanqun@126.com.
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- Read more about Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks
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Multimedia event detection (MED) is the task of detecting given events (e.g. birthday party, making a sandwich) in a large collection of video clips. While visual features and automatic speech recognition typically provide the best features for this task, non-speech audio can also contribute useful information, such as crowds cheering, engine noises, or animal sounds.
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- Read more about CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION
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Image processing and analysis techniques have drawn increasing attention since they enable a non-extractive and non-lethal approach to fisheries survey, such as fish size measurement, abundance prediction, catch estimation and compliance, species recognition and population counting. In this work, we present an innovative and effective method for measuring the chute-based fish length based on the morphological midline of the fish. The midline is generated through recursive morphological operations on the segmented fish mask.
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- Read more about Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks
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Multimedia event detection (MED) is the task of detecting given events (e.g. birthday party, making a sandwich) in a large collection of video clips. While visual features and automatic speech recognition typically provide the best features for this task, non-speech audio can also contribute useful information, such as crowds cheering, engine noises, or animal sounds.
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- Read more about Bandlimited Field Reconstruction from Samples obtained on a Discrete Grid with Unknown Random Locations
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Sampling spatial fields using sensors which are location unaware is an exciting topic. Due to symmetry and shift invariance of bandlimited fields, it is known that uniformly distributed location-unaware sensors cannot infer the field. This work studies asymmetric (nonuniform) distributions on location-unaware sensors that will enable bandlimited field inference. In this first exposition, to facilitate analysis, location-unaware sensors are restricted to a discrete grid. Oversampling is used to overcome the lack of location information.
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- Read more about Emotion Classification: How Does an Automated System Compare to Naive Human Coders?
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The fact that emotions play a vital role in social interactions, along with the demand for novel human-computer interaction applications, have led to the development of a number of automatic emotion classification systems. However, it is still debatable whether the performance of such systems can compare with human coders. To address this issue, in this study, we present a comprehensive comparison in a speech-based emotion classification task between 138 Amazon Mechanical Turk workers (Turkers) and a state-of-the-art automatic computer system.
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![](https://sigport.org/sites/default/files/styles/list/public/Media%20Root/coverEmotion_0.png?itok=vbo1DlGH)
- Read more about Emotion Classification: How Does an Automated System Compare to Naive Human Coders?
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The fact that emotions play a vital role in social interactions, along with the demand for novel human-computer interaction applications, have led to the development of a number of automatic emotion classification systems. However, it is still debatable whether the performance of such systems can compare with human coders. To address this issue, in this study, we present a comprehensive comparison in a speech-based emotion classification task between 138 Amazon Mechanical Turk workers (Turkers) and a state-of-the-art automatic computer system.
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![](https://sigport.org/sites/default/files/styles/list/public/Media%20Root/coverEmotion_0.png?itok=vbo1DlGH)
- Read more about Emotion Classification: How Does an Automated System Compare to Naive Human Coders?
- Log in to post comments
The fact that emotions play a vital role in social interactions, along with the demand for novel human-computer interaction applications, have led to the development of a number of automatic emotion classification systems. However, it is still debatable whether the performance of such systems can compare with human coders. To address this issue, in this study, we present a comprehensive comparison in a speech-based emotion classification task between 138 Amazon Mechanical Turk workers (Turkers) and a state-of-the-art automatic computer system.
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- Read more about Blind Deconvolution of Sparse But Filtered Pulses With Linear State Space Models
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- Read more about RATE OPTIMIZATION FOR MASSIVE MIMO RELAY NETWORKS: A MINORIZATION-MAXIMIZATION APPROACH
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