Edna  Bernhard

Edna Bernhard

1598280469

Speech Analytics, Sound Analytics in TorchAudio

The landscape of DataScience is changing everyday. In the last few years we have seen numerous number of research and advancement in NLP and Computer Vision field. But there is a field which is still unexplored and has a lot of potential , the field is — SPEECH.

In the last tutorial we have learned about:

  1. What is a Sound wave?

2. The basic properties of sound wave

3. Feature Extraction from sound wave

4. Pre-Processing a sound wave

In this tutorial we would be looking into the practical application of it in Python. The two most popular libraries to help you in your journey are:

  1. Librosa
  2. TorchAudio

TorchAudio — It is a PyTorch domain library consisting of I/O, popular datasets and common audio transformations that can bring new speed and efficiency to your PyTorch projects. It is one of the powerful speech modulation software created by Facebook.

#speech-analytics #speech-recognition #machine-learning-ai #pytorch #speech #ai

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Speech Analytics, Sound Analytics in TorchAudio
Edna  Bernhard

Edna Bernhard

1598280469

Speech Analytics, Sound Analytics in TorchAudio

The landscape of DataScience is changing everyday. In the last few years we have seen numerous number of research and advancement in NLP and Computer Vision field. But there is a field which is still unexplored and has a lot of potential , the field is — SPEECH.

In the last tutorial we have learned about:

  1. What is a Sound wave?

2. The basic properties of sound wave

3. Feature Extraction from sound wave

4. Pre-Processing a sound wave

In this tutorial we would be looking into the practical application of it in Python. The two most popular libraries to help you in your journey are:

  1. Librosa
  2. TorchAudio

TorchAudio — It is a PyTorch domain library consisting of I/O, popular datasets and common audio transformations that can bring new speed and efficiency to your PyTorch projects. It is one of the powerful speech modulation software created by Facebook.

#speech-analytics #speech-recognition #machine-learning-ai #pytorch #speech #ai

Jackson  Crist

Jackson Crist

1618209540

Measuring Crop Health Using Deep Learning – Notes From Tiger Analytics

Agrochemical companies manufacture a range of offerings for yield maximisation, pest resistance, hardiness, water quality and availability and other challenges facing farmers. These companies need to measure the efficacy of their products in real-world conditions, not just controlled experimental environments. Single-crop farms are divided into plots and a specific intervention performed in each. For example, hybrid seeds are sown in one plot while another is treated with fertilisers, and so on. The relative performance of each treatment is assessed by tracking the plants’ health in the plot where that treatment was administered.

#featured #deep learning solution #tiger analytics #tiger analytics deep learning #tiger analytics deep learning solution #tiger analytics machine learning #tiger analytics ml #tiger analytics ml-powered digital twin

Daron  Moore

Daron Moore

1598411940

Speech Analytics Part -1, Basics of Speech Analytics

So what is a sound wave ?

Speech signals are sound signals, defined as pressure variations travelling through the air. These variations in pressure can be described as waves and correspondingly they are often called sound waves.

**Sound **wave can be described by five characteristics: Wavelength, Amplitude, Time-Period, Frequency and Velocity or Speed

  1. **Wavelength **— The minimum distance in which a sound wave repeats itself is called its wavelength. That is it is the length of one complete wave. It is denoted by a Greek letter λ
  2. **Amplitude **— When a wave passes through a medium, the particles of the medium get displaced temporarily from their original undisturbed positions. The maximum displacement of the particles of the medium from their original undisturbed positions, when a wave passes through the medium is called amplitude of the wave.

3. **Time Period **— The time required to produce one complete wave or cycle or cycle is called time-period of the wave.

4. **Frequency **— The number of complete waves or cycles produced in one second is called frequency of the wave. The S.I unit of frequency is hertz or Hz.

5. **Velocity **— The distance traveled by a wave in one second is called velocity of the wave or speed of the wave.

What is Windowing and Sampling in Sound Data?

Sampling the signal is a process of converting an analog signal to a digital signal by selecting a certain number of samples per second from the analog signal. Can you see what we are doing here? We are converting an audio signal to a discrete signal through sampling so that it can be stored and processed efficiently in memory.

#ai #speech-recognition #speech-analytics #pyaudio #machine-learning

Alteryx Provides Free Access to Its Data Science Courses for Recent Graduates

The overnight transformation of companies adopting new technologies and transitioning to a digital work environment amid pandemic has made upskilling the most critical component in a worker’s repertoire in 2021. While information, data and the ability to make the right decisions serve as a stabiliser across verticals, analytics and data science have become indispensable tools to navigate today’s career scene.

According to a recent Forrester study, the top two challenges decision-makers cited are — the lack of employees with data skillsets and the lack of skills among business users who must use data insights. Almost 66% of organisations believe there is a requirement for data literacy among employees, where 59% demand analytic efficiency. However, with a converged approach to analytics through democratising access to data, automating tedious and complex processes, and promoting upskilling of data and knowledge workers, organisations can create a thriving data and analytics culture within.

#featured #advancing data and analytics #alteryx adapt #alteryx advancing data and analytics #alteryx upskilling programs #analytics upskilling #data and analytics #data science and analytics #start your analytics journey with adapt

Chet  Lubowitz

Chet Lubowitz

1598668320

Speech Analytics Part -3, Creating a SpeechToText Classifier

Making a sound model is very similar to making a Data, NLP or Computer Vision. The most important part is to understand the Basics of sound wave and how we pre-process it to put it in a model.

You can check out the previous Part1 and Part2 of this series to know how we work with a sound wave.

We would be using the DataSet used in this competition to create our SpeechToText Classifier. The Dataset consists of several occurrences of 12 words -”yes” ,”no”,” up”,” down”,” left”,” right”,” on”,” off”,” stop” ,”go”,” silence” and unknown sounds. Given we would be creating a Classifier model the model would only be able to predict either of the 12 words.

You can find the entire Notebook Here

Step 1: Browsing Sub Folders to read our Sound files

Step 2: Chopping and Padding Sound Files

One key requirement for a Classifier model is that the Input length of every word has to be of the same length. Thus we would make sure to chop extra time and pad silence at the end of words whose occurrence time is not equal to 16 sec.

Step 3: Feature Extraction from sound wave

To learn in depth on this please refer to my previous Blog. For this problem we would be extracting the MFCC features of the sound and using them as an input for my Classifier model.

Step 4: Model Architecture

Given every word consist of Phonemes, the first step which the model needs to do is extract necessary features/phonemes out of the entire word. Thus we would be using a CNN model to capture these features. In the next step we need to look into all the features/phonemes and classify the word into one of the categories. The sequence plays an important role out here. Thus we would add an LSTM layer too.

The final architecture looks like:

#machine-learning #speech-to-text-api #pyaudio #speech-recognition #speech-analytics