Flavor-based music retrieval from audio embeddings

The relationship between sound and taste has been studied in neuroscience and psychology for decades, revealing that certain acoustic profiles can evoke sensations such as sweetness, sourness, or bitterness. However, this connection has barely been exploited in multimedia content retrieval systems. Recent research has formalized the prediction of taste from audio as a content-based music retrieval problem, using embeddings generated by pre-trained encoders such as VGGish within multi-task regression architectures. Results show that these models can approximate the consensus of a group of human evaluators with an error less than half the typical deviation of a single evaluator, greatly surpassing previous baselines. This breakthrough opens the door to innovative applications, from music recommendation systems that pair songs with dishes or beverages to immersive environments that adjust the listening experience according to taste preferences. Implementing such solutions requires a comprehensive technological approach combining artificial intelligence, cloud infrastructure, and robust software development. For example, processing large volumes of audio and real-time inference require scalable platforms like those offered by AWS and Azure cloud services. Furthermore, integrating predictive models into commercial applications demands artificial intelligence for businesses specifically designed for each use case. At Q2BSTUDIO we develop custom software that connects auditory data analysis with cybersecurity modules and Power BI dashboards, ensuring that each component—from the backend to the user layer—works cohesively. Creating AI agents or recommendation systems based on audio embeddings is one of the many areas where our custom application capabilities can make a difference. If your organization seeks to explore these technological frontiers, our team is ready to design solutions that transform multimodal data into real competitive advantages.