A Deep Learning model that classifies digits based on its Audio Input.
Audio is a term used to describe any sound or noise in a range the human ear is capable of hearing. Measured in hertz, the audio signal on a computer is generated using a sound card and heard through speakers or headphones.
Any digital information with speech or music stored on and played through a computer is known as an audio file or sound file. One of the most common types of audio file formats used today is the MP3.
An audio signal is a representation of sound, typically using either a changing level of electrical voltage for analog signals, or a series of binary numbers for digital signals.
To plot all these waveplots, I came up across an exciting python library librosa. librosa is a python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems.
These are the waveplots of the digits when we spell them. Please note that we are spelling one or any other digit as it should be spelled, not like oooooaannee or something like that. You can also try it once. Its funny though. 😆😆😆😆
This is the countplot of the audio data. The X-axis represents the digits 0-9 and the Y-axis represents the count of respective digit.
We can note that the dataset is equally distributed i.e. they are balanced.
Layer 0: Input of 40 dimensionalLayer 1: Dense layer of 128 neurons followed by ReLU Activation and Dropout rate of 0.25.Layer 2: Dense layer of 256 neurons followed by ReLU Activation and Dropout rate of 0.25.Layer 3: Dense layer of 128 neurons followed by ReLU Activation and Dropout rate of 0.25.Layer 4: The softmax layer for the classifiaction of 10 different digits.
The below is the pictoral representation of the model architecture.
The model was trained over 200 epochs. Here are the accuracy and loss curves.
From the above figure we can say that the model is not overfitting.
