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First, a convolutional neural network (CNN) with densely-connected CNN blocks is built as our base network.
With this belief, focusing on the fact that the DWT has an anti-aliasing filter and the perfect reconstruction property, we design the proposed layers.
Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song. Such components include voice, bass, drums and any other accompaniments.
We consider statistical models of estimation of a rank-one matrix (the spike) corrupted by an additive gaussian noise matrix in the sparse limit.
3D HUMAN POSE ESTIMATION 3D OBJECT DETECTION 3D SEMANTIC INSTANCE SEGMENTATION ASPECT-BASED SENTIMENT ANALYSIS COMPRESSIVE SENSING CONDITIONAL IMAGE GENERATION DATA-TO-TEXT GENERATION DEPENDENCY PARSING FACTUAL VISUAL QUESTION ANSWERING FEW-SHOT SEMANTIC SEGMENTATION IMAGE INPAINTING LANGUAGE MODELLING LOW RESOURCE NAMED ENTITY RECOGNITION MACHINE TRANSLATION MULTI-PERSON POSE ESTIMATION MUSIC SOURCE SEPARATION NODE CLASSIFICATION ONE-SHOT OBJECT DETECTION QUESTION GENERATION RETINAL VESSEL SEGMENTATION SCENE TEXT DETECTION SELF-SUPERVISED ACTION RECOGNITION SEMANTIC SEGMENTATION SENTENCE COMPRESSION SENTENCE EMBEDDINGS FOR BIOMEDICAL TEXTS TRAFFIC PREDICTION UNSUPERVISED DOMAIN ADAPTATION VIDEO FRAME INTERPOLATION VIDEO RETRIEVAL WEAKLY SUPERVISED OBJECT DETECTION
They are trained on synthetic mixtures of audio made from isolated sound source recordings so that ground truth for the separation is known.
In this paper, we present the synthesized Lakh dataset (Slakh) as a new tool for music source separation research.
Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source separation.
To reach information at remote locations, we propose to combine dilated convolution with a modified version of gated recurrent units (GRU) called the `Dilated GRU' to form a block.