Amazon researchers have developed a unique multi-stage method for automatic instrumental music detection in large-scale music catalogs. The method includes separating vocals and accompaniment, quantifying singing voice content, and analyzing the background track. The researchers compared their approach to existing models and found high precision and recall in identifying instrumental music. This development is significant for music streaming services seeking to differentiate between instrumental and vocal music.
In the world of music streaming services, distinguishing between instrumental music and vocal music is a major challenge. Researchers from Amazon have addressed this issue and proposed a unique approach for detecting instrumental music in a large music catalog.
The researchers found that traditional methods for instrumental music detection have their limitations. Thus, they introduced a multi-stage method consisting of three main stages.
First, they used a source separation model to divide the audio into the vocals and background music. This differentiation is crucial because instrumental music should not include any vocal components.
Next, the vocal signal’s singing voice content was quantified. This helped determine whether a track has vocals or not. If the level of singing voice falls below a certain threshold, it implies that the recording is instrumental.
Lastly, the background music was analyzed using a neural network trained to identify instrumental components. If the quantity of singing voice falls below the threshold, a binary classifier is used to determine whether the music is instrumental based on the presence or absence of musical instruments in the background recording.
This multi-stage approach effectively determines if the music is instrumental by considering both the presence of singing voice and the features of the background music. The research illustrates the superiority of this method compared to existing models by achieving high precision and recall in identifying instrumental music across a large music catalog.
Overall, the proposed approach is a significant advancement for automatically detecting instrumental music in the context of music streaming services. You can find the complete research paper in the link provided.
Action items:
1. Research and gather more information on the multi-stage method for instrumental music detection proposed by the Amazon researchers.
2. Evaluate the potential application of the multi-stage method for instrumental music detection in our music streaming services.
3. Consider the integration of the multi-stage method into our music catalog to improve the identification of instrumental music.
4. Assess the feasibility and potential impact of implementing the multi-stage method in our current music categorization and playlist building processes.
5. Explore collaboration opportunities with the Amazon research team to further investigate and develop the multi-stage method for instrumental music detection.Note: As the meeting notes do not mention specific individuals responsible for each action item, it would be advisable to allocate these tasks to appropriate team members based on their expertise and workload.
List of Useful Links:
- AI Scrum Bot – ask about AI scrum and agile
- Amazon Researchers Introduce a Novel Artificial Intelligence Method for Detecting Instrumental Music in a Large-Scale Music Catalog
- MarkTechPost
- Twitter – @itinaicom
