I'm sure more than once we've picked up our cell phone to find out what song is playing in a bar or in a series we're watching on TV. Today it is a gesture that we usually do without being aware of the technology behind applications such as Shazam , for example. This technology is known as “Audio Fingerprinting” , and basically consists of identifying an audio file by extracting unique characteristics from its signal. Audio fingerprinting is a technique used to identify a particular audio recording. This technique converts an audio recording into a unique set of data that is used to identify the recording. The audio fingerprinting process involves several steps, such as extracting features from the audio, creating an audio fingerprint, and searching for matches in a database.
Sound recognition has become an important task in many fields, such as security, music and advertising . However, sound recognition remains a challenge due to the complexity of sounds and the variety of sound sources in the environment . One of the most common applications is identifying songs on the radio or in an online playlist. The audio fingerprinting technique is used to identify the song based on the unique characteristics of the audio recording. With this technology, songs can be identified automatically, allowing you to identify which song is playing and providing information such as the title, artist and album associated with the song . But it can also be very useful in detecting songs that have been uploaded illegally, by comparing the fingerprints of the songs with the fingerprints of the legal songs in the database.
"Audio fingerprinting has been studied for some time, but there are certain scenarios where it does not work well. I am referring to noisy environments such as concerts, bars, outdoor shows, or also in environments where music plays in the background, as is often the case on radio and television»
Developing a music monitoring system is a technological challenge, especially when considering the noisy settings in which music is often heard, such as concerts, bars or outdoor shows. To solve this problem, the NextCore project of the BMAT company was born, which has created an innovative technology for real-time music monitoring. The company was born in 2005 as a spin-off of the MTG (Music Technology Group ) research group of the Pompeu Fabra University ( UPF ). Research has always been present in the company and has played a decisive role in its success, as confirmed by its various patents and publications. BMAT has already started two Industrial Doctorates, the first project was carried out by industrial doctor Blai Meléndez Catalán, a successful project that has developed a technology that has already been incorporated into the services offered by the company.
Guillem Cortès Sebastià is the doctoral student of the second Industrial Doctorates project that is carried out at BMAT in collaboration with the UPF, under the supervision of professor Xavier Serra and doctor Emilio Molina . The aim of the project is to investigate how to improve music monitoring with deep learning algorithms. Cortès has always had an intense relationship with music, until discovering the importance of the relationship between music and mathematics. Music and mathematics are closely related. In fact, many of the fundamental aspects of music, such as rhythm, melody, harmony, and form, can be described and understood using mathematical concepts. Cortès was surprised to discover how the sounds we hear in music and mathematics are related. To give an example, the note "A3" which is often used as a reference note for tuning, emits a sound wave that vibrates at a frequency of 440 Hz. If they play the same note, but an octave higher, the so-called "A4", the frequency is 880 Hz, twice the first. This also happens with the chords that sound good to our ears, the frequencies of the notes that make them up have a simple mathematical relationship.
"The big challenge that BMAT poses is to use Audio Fingerprinting in noisy scenarios and in situations where music plays in the background, as often happens on radio and television"
Cortès tells us that the aim of the project is to improve music monitoring systems and make them more robust in multiple scenarios, as well as to create tools to encourage research in this field: "Audio Fingerprinting has long been it is studied, but there are certain scenarios where it does not work well. I am referring to noisy environments such as concerts, bars, outdoor shows, or also in environments where music plays in the background, as happens very often on radio and television". The monitoring system of the NextCore project is based on the Audio Fingerprinting technology that was explained at the beginning of this article, and the big challenge for BMAT is to use Audio Fingerprinting in noisy scenarios and in situations where what music plays in the background, as is often the case on radio and television.
Thanks to the collaboration between BMAT and the UPF, the NextCore project can change the way music monitoring systems adapt to noisy environments. This research, which combines music and mathematics, has the potential to encourage the development of new tools in the field of audio identification.
