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 »
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 »
Audio data is utilized in various real-life applications across industries. Here are some examples of how audio data is leveraged in machine learning and AI: Student researcher and Rise Global Winner Chandra Suda invented a tool in 2023 for screening …
Real-life applications for labeling audio data – Exploring Audio Data-1 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 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 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 …
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 …
The choice of loss function, whether it’s binary cross-entropy (BCE) or mean squared error (MSE), depends on the nature of the problem you’re trying to solve with an autoencoder. BCE is commonly used when the output of the autoencoder is …
A hands-on example to label video data using autoencoders – Labeling Video Data-2 Read more »