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 …
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 »
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 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 »