Softmax classification

Multilabel classification

We have talked about two types of regression: linear and binary
We also covered how to use gradient descent to optimize a linear regressor and a logistic (i.e. binary) classifier.
Linear and binary classification are extremelly powerful. However, sometimes we want to separate data into C distinct categories. This is known as multilabel (or multiclass) classification.
As an example, imagine you want to classify the different vowels of human speech.

The Persian Consonant Vowel Combination (PCVC) dataset contains audio for persian vowels, pronounced in combination with a preceding consonant.
Imagine that we want to use this dataset to develop a classification algorithm that can hear and identify which persian vowel is being said.
You can read more about this dataset in the original publication.
- Malekzadeh, S., Gholizadeh, M.H. Ghayoumi zadeh H., and Razavi, S.N., 2018. Persian phonemes recognition using PPNet. arXiv preprint arXiv:1812.08600.

Softmax is a function that allows you take an input $x_i \in \mathbb{R}^{1xD}$ and use it to generate “probabilities” across each of the C classes.
The equation of softmax is $softmax(\theta_i) = \frac{e^{\theta_i}}{\sum_je^{\theta_i}} \in \mathbb{R}^{1xC}$
If we want to use softmax to classify datapoints $x_i \in \mathbb{R}^{1xD}$ , then $\theta_i = x_iW - max_j(x_iW) \in \mathbb{R}^{1xC}$ (where $max(x_iW)$ is subtracted for computational stability purposes) and $W \in \mathbb{R}^{DxC}$ is our matrix with the parameters we are optimizing to carry out classification.

Last class we saw that we can use the binary cross entropy loss to optimize a logistic regression classifier.
To optimize a softmax (i.e. multiclass) classifier, we will use the categorical cross entropy loss $J = -\frac{1}{N}\sum_{i=1}^{N}y_ilog(softmax(\theta_i))$ .
$y_i \in \mathbb{R}^{1xC}$ , the ground truth, is a one-hot vector, where only an entry is the number $1$ and all other entries are $0$ . Each index in $y_i$ represents a different category. The index with the number $1$ indicates which class the corresponding datapoint (i.e. $x_i$ ) truly belongs to.

So far, the optimization routines we have implemented (linear and logistic regression) can give different solutions for the parameters.
This is due to the objective function being only constrained by the comparison between a predicted value $\hat{y}_i$ and its ground truth $y_i$ .
We can add a term to the loss to regularize the parameters $W$ , so that we impose a condition over the type of values that can be present in $W$ .
The most common type of regularization is the squared L2 norm, which results in the loss function $J = -\frac{1}{N}\sum_{i=1}^{N}ylog(x_iW) + \lambda\sum_{d=1}^{D}\sum_{c=1}^{C}W^2$ , where $\lambda$ is the “regularization strength” term.
There are other types of regularizaton though.

Due Mar 15 at 11:59PM (Eastern Standard Time)

When you are done, you MUST post a picture on our course subreddit with the performance and confusion matrix you got on the evaluation set (example)

In this homework you will work with raw audio signals.
Standardizing audio signals is challenging when the number of datapoints is limited.
What would happen if we standardize a raw audio samples as we standardized other features?
As a result, our standarization will have to be different.
For this assignment, we will standardize each datapoint to have samples with zero mean and floating-point values normalized to be in range of -1 and 1.
- Why is this a good standarization when working with raw audio (specially when datapoints are limited)?

When training data is limited, we can use a few techniques to expand the number of datapoints.
To expand the number of audio datapoints we can create copy of the training data to:
- mix it with different levels of noise to simulate “noisy” samples.
  - noise can also be added as a series of bursts instead of using uniform noise across samples in a datapoint.
- shift the pitch of each datapoint using an effect like librosa.effects.pitch_shift.
- convolve with an impulse response to simulate different acoustic conditions.
- filter with diverse types of digital filters.
- compress with an audio format like mp3.
The project audiomentations provides you with readily-available functions to augment audio data.