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 …
Best practices for audio format conversion – Exploring Audio Data Read more »
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 »
Librosa is a versatile Python library that empowers researchers, data scientists, and engineers to explore and manipulate audio data with ease. It provides a range of tools and functions that simplify the complexities of audio analysis, making it accessible to …
Audio data fundamentals – Exploring Audio Data-2 Read more »
First, let us understand some basic terminology in audio data analysis: So, RMS energy is a way to take the raw amplitudes of an audio signal, square them to focus on their intensity, find the average of these squared values, …
Audio data fundamentals – Exploring Audio Data-1 Read more »
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: …
Advances in video data labeling and classification – Labeling Video Data-1 Read more »
Time complexity: The basic Watershed algorithm has a time complexity of O(N log N), where N is the number of pixels in the image. This complexity arises from the sorting operations involved in processing the image gradient. Space complexity: The …
The overall purpose of these steps is to preprocess the image and create a binary image (sure_bg) that serves as a basis for further steps in the watershed algorithm. It helps to distinguish the background from potential foreground objects, contributing …
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 …
A hands-on example to label video data using autoencoders – Labeling Video Data-1 Read more »