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Found 9 papers, 4 papers with code
Future Trends in the Design of Memetic Algorithms: the Case of the Linear Ordering Problem
The way heuristic optimizers are designed has evolved over the decades, as computing power has increased.
Robust Wake-Up Word Detection by Two-stage Multi-resolution Ensembles
It employs two models: a lightweight on-device model for real-time processing of the audio stream and a verification model on the server-side, which is an ensemble of heterogeneous architectures that refine detection.
Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning
Many real-time applications (e. g., Augmented/Virtual Reality, cognitive assistance) rely on Deep Neural Networks (DNNs) to process inference tasks.
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda Networks
This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task.
Speech Enhancement for Wake-Up-Word detection in Voice Assistants
Keyword spotting and in particular Wake-Up-Word (WUW) detection is a very important task for voice assistants.
Enabling Zero-shot Multilingual Spoken Language Translation with Language-Specific Encoders and Decoders
On the other hand, Multilingual Neural Machine Translation (MultiNMT) approaches rely on higher-quality and more massive data sets.
Seeing and Hearing Egocentric Actions: How Much Can We Learn?
Our interaction with the world is an inherently multimodal experience.
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
End-to-end models for raw audio generation are a challenge, specially if they have to work with non-parallel data, which is a desirable setup in many situations.
How Much Does Audio Matter to Recognize Egocentric Object Interactions?
Sounds are an important source of information on our daily interactions with objects.
