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Found 6 papers, 1 papers with code
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration
In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration.
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet
This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome.
Few-shot Class-incremental Audio Classification Using Stochastic Classifier
It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only.
Few-Shot Speaker Identification Using Lightweight Prototypical Network with Feature Grouping and Interaction
In the proposed embedding module, audio feature of each speech sample is split into several low-dimensional feature subsets that are transformed by a recurrent convolutional block in parallel.
Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention
Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification.
Domestic activities clustering from audio recordings using convolutional capsule autoencoder network
Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster.
