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Found 12 papers, 4 papers with code
Lightweight Speaker Verification Using Transformation Module with Feature Partition and Fusion
The features that are output from current block of the model are processed according to the steps above before they are fed into the next block of the model.
Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network
Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into pre-defined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment.
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.
Speaker verification using attentive multi-scale convolutional recurrent network
The proposed method is compared with state-of-the-art methods for speaker verification.
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 Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes
Labeled support samples and unlabeled query samples are used to train the prototype adaptation network and update the classifier, since they are informative for audio classification.
Domestic Activity Clustering from Audio via Depthwise Separable Convolutional Autoencoder Network
Automatic estimation of domestic activities from audio can be used to solve many problems, such as reducing the labor cost for nursing the elderly people.
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.
Sound Event Detection with Depthwise Separable and Dilated Convolutions
The number of the channels of the CNNs and size of the weight matrices of the RNNs have a direct effect on the total amount of parameters of the SED method, which is to a couple of millions.
