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, …

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The integration of audio analysis with other data types allows for the development of comprehensive AI applications that leverage multiple modalities. Here are some real-world applications where the integration of audio analysis with other data types is beneficial: Integration: We …

Real-life applications for labeling audio data – Exploring Audio 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 …

Computational complexity – Labeling Video Data Read more »

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 …

A hands-on example to label video data segmentation using the Watershed algorithm – Labeling Video Data-2 Read more »

In this example code, we will implement the following steps: Let’s read the video data from the input directory, extract the frames for the video, and then print the original video frame: video_path = “/datasets/Ch9/Kinetics/dance/dance3.mp4”Check if the file existsif os.path.exists(video_path):cap …

A hands-on example to label video data segmentation using the Watershed algorithm – Labeling Video Data-1 Read more »

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 »