Audio data cleanup is essential to enhance the quality and accuracy of subsequent analyses or applications. It helps remove unwanted artifacts, background noise, or distortions, ensuring that the processed audio is more suitable for tasks such as speech recognition, music …
Example code for audio data cleaning – Exploring Audio Data Read more »
When working with audio data in the industry, there are several common best practices for converting audio to the correct format and performing cleaning or editing tasks. The following are some steps and recommendations. Tools for conversion include FFmpeg, a …
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Before diving into audio data analysis with Librosa, you’ll need to install it. To install Librosa, you can use pip, Python’s package manager:pip install librosa This will download and install Librosa, along with its dependencies. Now that you have Librosa …
Example code for loading and analyzing sample audio file – Exploring Audio Data Read more »
Imagine a world without music, without the sound of your favorite movie’s dialog, or without the soothing tones of a friend’s voice on a phone call. Sound is not just background noise; it’s a fundamental part of our lives, shaping …
While generative models can be trained in a self-supervised manner, not all generative AI is self-supervised, and vice versa. Generative models can be trained with or without labeled data, and they can use a variety of training paradigms, including supervised, …
Advances in video data labeling and classification – Labeling Video Data-2 Read more »
The field of video data labeling and classification is rapidly evolving, with continuous advancements. Generative AI can be applied to video data analysis and labeling in various use cases, providing innovative solutions and enhancing automation. Here are some potential applications: …
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The Watershed algorithm is a popular technique used for image segmentation, and it can be adapted to label video data as well. It is particularly effective in segmenting complex images with irregular boundaries and overlapping objects. Inspired by the natural …
Using the Watershed algorithm for video data labeling – Labeling Video Data Read more »
Using a pre-trained autoencoder model to extract representations from new data can be considered a form of transfer learning. In transfer learning, knowledge gained from training on one task or dataset is applied to a different but related task or …
In this example, a threshold value of 0.5 is used. Pixels with values greater than the threshold are considered part of the foreground, while those below the threshold are considered part of the background. The resulting binary frames provide a …
A hands-on example to label video data using autoencoders – Labeling Video Data-3 Read more »
Let’s see some example Python code to label the video data, using a sample dataset: Let’s import the libraries and define the functions: import cv2import numpy as npimport cv2import osfrom tensorflow import keras Let us write a function to load …
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