Convolution - Bitwig Audio FX Guide (Patreon)
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"Convolution is a mathematical operation that is commonly used in the field of digital signal processing. In the context of audio, convolution is used to produce a filtered version of an input signal. In a convolutional neural network, convolution is used to extract features from the input signal and build a model that can be used to make predictions or classify the input data.
When working with audio, convolution is typically performed by first dividing the input signal into short segments, called frames. Each frame is then multiplied by a small, fixed-sized filter, called a kernel. The result of this multiplication is called a feature map, which encodes the presence of certain features in the input signal. These feature maps are then processed by other layers in the network to make predictions or classify the input data.
Convolutional neural networks are often used for tasks such as speech recognition, music classification, and sound event detection. By using convolution to extract features from the input signal, these models are able to learn the underlying patterns and structures in the data, and make more accurate predictions or classifications."