Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Content-based audio classification and segmentation by using support vector machines
Content-based audio segmentation using support vector machines
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures
Evaluation of Frequently Used Audio Features for Classification of Music into Perceptual Categories
CNN architectures for large-scale audio classification
Transfer Learning by Supervised Pre-training for Audio-based Music Classification
Automatic Musical Genre Classification of Audio Signals
Multiscale Scattering for Audio Classification
Automatic audio segmentation using a measure of audio novelty
Musical genre classification of audio signals
Speech/Music Classification using SVM
audio feature extraction and analysis for scene segmentation and classification .
Content-based Identification of Audio Material Using MPEG-7 Low Level Description
Convolutional gated recurrent neural network incorporating spatial features for audio tagging
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
Acoustic Scene Classification Using a CNN-SuperVector System Trained with Auditory and Spectrogram Image Features.
Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network
Automatic audio content analysis
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals
Importance of audio feature reduction in automatic music genre classification
Combining Temporal Features by Local Binary Pattern for Acoustic Scene Classification
An End to End Model for Automatic Music Generation: Combining Deep Raw and Symbolic Audio Networks
G-Free: defeating return-oriented programming through gadget-less binaries
Ulerythema ophryogenes: updates and insights.
Prevalence of gastrointestinal symptoms in patients with influenza, clinical significance, and pathophysiology of human influenza viruses in faecal samples: what do we know?
An overview of audio information retrieval