Modern AI-based noise cancellation algorithms generally perform well in most common environments. However, there are specific scenarios where their effectiveness may fall short. These unique environments often involve distinct noise profiles, such as the noise generated by specific machinery. In such cases, the performance of generic noise cancellation algorithms may not meet the desired quality standards. To address these specific scenarios, it becomes crucial to enhance and optimize noise cancellation techniques to effectively target and mitigate the particular noise characteristics associated with these environments. By focusing on these specific noise sources, tailored noise cancellation solutions can be developed to deliver superior performance and ensure a high-quality audio experience in these specialized scenarios.

Overall, improving noise cancellation for specific noise types enables more tailored and effective noise reduction solutions, leading to better user experiences, clearer communication, optimized performance in noise-sensitive environments, and increased adaptability in audio processing systems.

Enhancing noise cancellation in audio often entails training a model to accurately identify and suppress specific types of noise. The process begins by meticulously assembling an extensive dataset of audio recordings that encompass the targeted noise you intend to eliminate. This dataset serves as the foundation for constructing and training a specialized network tailored to effectively attenuate the identified noise. By following this iterative process of data collection, model development, and training, you can progressively refine the noise cancellation capabilities of the network, ultimately achieving superior noise reduction results in the desired audio recordings.

The capability to optimize & tune the noise cancelling AI engine is not offered by many vendors. If you find yourself in a need for this capability, SoliCall offers versatile noise cancelling software that can be tuned & optimized to specific usage environments.