Building AI Course Project
SoundMatch is an AI-driven audio recognition platform aimed at discovering a match in identifying an artist by audio. This innovative solution addresses the common frustration of not being able to identify songs or an artist, offering a quick and accurate way to not be spending hours upon the internet to hopefully find what you where looking for in the first place.
Many of us have experienced the frustration of hearing a great song on the radio or at a live event and being unable to identify the singer or artist. This common scenario highlights the need for a solution that can quickly and accurately recognize songs and artists from audio snippets.
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Frequency of the Problem: The problem of not being able to identify songs or artists in real-time audio situations is a frequent one. It occurs daily for music enthusiasts, radio listeners, and event attendees who encounter unfamiliar tracks.
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Personal Motivation: As a passionate music enthusiast deeply immersed in the music industry, the inability to identify and connect with music in the moment served as a personal motivator. The desire to provide a solution for this widespread challenge has driven the pursuit of this project.
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Importance and Interest: The topic is important because it bridges the gap between what we hear and what we know, enhancing our music discovery experience. It also has the potential to be of interest to a broad audience, including music fans, radio stations, DJs, and music industry professionals, making it a compelling and significant endeavor.
SoundMatch simplifies the process of identifying artists by audio in various situations:
- Live Events: Attendees at concerts or festivals can use their smartphones to record a snippet of a song and instantly discover a match of what like to be the artist.
- Radio Listeners: Anyone listening to the radio can identify a song or artist without the need for a DJ announcement.
- Music Enthusiasts: Users can use SoundMatch to identify songs in social settings, such as parties or gatherings.
Users can access SoundMatch through a user-friendly web or mobile interface, where they can upload an audio snippet or record one in real-time. The AI system then processes the audio and provides the artist's name, making it a seamless and enjoyable music discovery experience.
SoundMatch relies on a large dataset of audio samples from various artists. This dataset is collected and annotated for supervised learning. The AI model is designed using deep learning techniques, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), trained on the annotated dataset. The AI system uses audio feature extraction methods like spectrograms or Mel-frequency cepstral coefficients (MFCCs) to process the audio data and make artist predictions. This project is aimed at creating an AI-driven audio recognition platform, similar to how Shazam works, but on a broader scale.
SoundMatch is a powerful tool for identifying artists by audio, but it has certain limitations and ethical considerations:
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Limited Catalog: The accuracy of artist identification depends on the availability of songs in the database. It may not work for very obscure or new artists.
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Copyright and Privacy: Use of copyrighted audio samples must adhere to legal regulations and privacy considerations, especially when user-generated audio is involved and limitations by GDPR.
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Bias in Data: Ensuring a diverse and representative dataset is crucial to avoid bias in artist identification.
To further develop SoundMatch and make it even more robust, the following is needed:
- Expansion of Catalog: Continuously update the database with new music releases and lesser-known artists.
- Feedback Loop: Implement a feedback mechanism to improve the accuracy of artist identification based on user feedback.
- Partnerships: Collaborate with music streaming services to enhance the platform's capabilities.
- Scalability: Design the system to handle a growing user base effectively.
- https://www.shazam.com/
- Image made in Canva
- Inspiration from real-time audio recognition services and the desire to enhance music discovery.