Image Classifier for Music Metadata Retrieval
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Solution Overview
Problem
Existing technologies lack the ability to automatically identify and retrieve music or metadata associated with a song, genre, instruments, or artist based on an image, limiting users' ability to search for digital versions of albums or music based on album cover art or artist images.
Innovation Solution
A system and method that processes images using a classifier, such as a neural network, to determine the likelihood of an image belonging to a specific classification, and accesses corresponding content, including metadata or audio tracks, by generating classification confidence scores and bounding box confidence scores, allowing for the retrieval of musical content like playlists or metadata based on image inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If audio-based identification systems are used, then music identification capability is provided, but the system cannot identify content based on images
Solution Approach 1:
The system extends content identification functionality to handle multiple input types (audio samples and images) through a unified classifier architecture. The same classifier infrastructure that processes audio-based features is adapted to process image-based features, enabling the system to perform both audio-based music identification and image-based content retrieval through a single multi-functional platform
2Measurement precision
If manual searching is used, then users can find music metadata, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary classification of images into musical categories (genres, artists, albums) before the user needs to search for specific metadata. By pre-organizing content based on image classification results, the system eliminates the need for manual searching and directly retrieves relevant metadata and audio tracks when an image is provided
Solution Approach 2:
The system replaces manual searching mechanics with automated image-based classification and content retrieval. Instead of requiring users to manually browse and search through music databases, the classifier automatically processes the input image, determines the relevant musical category, and retrieves the corresponding metadata and content through automated system operations
3Productivity
If simple classification is used, then processing speed is maintained, but classification accuracy for musical categories decreases
Solution Approach 1:
The system adjusts classification parameters and confidence thresholds dynamically based on the input image characteristics and the specific musical category being evaluated. By optimizing classification parameters for different scenarios while maintaining efficient processing through the established classifier architecture, the system achieves both high processing speed and accurate musical category classification
Data Source
AI summary
A system, method and computer program product for accessing content. The method comprises processing at least one image with a classifier, and, in response to the at least one image being processed by the classifier, outputting from the classifier a value indicative of the likelihood that the at least one image belongs to at least one classification. The method also comprises determining whether the at least one image belongs to the at least one classification, based on the value, and accessing predetermined content when it is determined that the at least one image belongs to the at least one classification. Images may be classified by, e.g., genre, musical album, concept, or the like, and, in cases where an image belongs to any such classes, predetermined content (e.g., metadata and/or an audio track) relating thereto is identified and presented to the user.