VGGish

• CNN Architectures for Large-Scale Audio Classification
• applying various state-of-the-art image networks with CNN architectures to audio and show that they are capable of excellent results on audio classification
• examine fully connected deep neural networks such as AlexNet, VGG, InceptionNet, and ResNet
• The input audio is divided into non-overlapping 960 ms frames which are decomposed by applying the Fourier transform, resulting in a Spectrogram
• Spectrogram is integrated into 64 mel-spaced frequency bins, and the magnitude of each bin is log-transformed
• gives log-mel Spectrogram patches that are passed on as input to all classifiers
• Acoustic Event Detection
• train a classifier on embeddings learned from the video-level task on AudioSet
• model for AED with embeddings learned from these classifiers does much better than raw Features on the Audio Set AED classification task
• derivatives of image classification networks do well on the audio classification task
• increasing the number of labels they train on provides some improved performance over subsets of labels
• performance of models improves as they increase training set size,
• model using embeddings learned from the video-level task do much better than a baseline on the AudioSet classification task