Sequence-Aligned Estimation Models for Karaoke Song Recommendations
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Solution Overview
Problem
Existing systems fail to appropriately consider the sequence in which users sing musical pieces during karaoke, leading to inadequate recommendations for subsequent songs, especially in group settings.
Innovation Solution
A system that generates a model for estimation using machine learning to align audio data of musical pieces in sequence order, performing convolution with a kernel to account for the sequence of sung pieces, allowing for robust estimation and recommendation of subsequent songs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation methods based on singing history are used, then musical pieces can be recommended to users, but the sequence in which users sing musical pieces is not appropriately taken into account
Solution Approach 1:
The patent transforms the one-dimensional singing history data into a two-dimensional sequence space by aligning audio data of multiple musical pieces in sequence order. This dimensional transformation allows the model to capture temporal relationships and sequence patterns that were previously lost, enabling accurate estimation of next song recommendations while preserving the sequential context of karaoke sessions.
2Reliability
If machine learning with sequence alignment is implemented, then sequence characteristics are appropriately taken into account, but the system complexity increases
Solution Approach 1:
The patent extracts the essential sequence characteristics from the aligned audio data through convolution operations with kernels designed to capture temporal patterns. By separating the sequence analysis function into a dedicated convolutional layer, the model achieves reliable estimation of singing sequences while maintaining a modular architecture that manages complexity effectively.
Solution Approach 2:
The patent replaces traditional sequence analysis methods with a learned convolutional model that automatically discovers temporal patterns in singing sequences. This substitution of mechanical rule-based systems with adaptive machine learning models improves reliability while the modular convolutional architecture keeps the system complexity manageable through parameter sharing and translation invariance.
Data Source
AI summary
A system of generating a model for estimation is a system that generates the model for estimation used for estimation based on information about musical pieces, the system including: a learning data acquiring unit configured to acquire learning information in which information about audio data of a plurality of musical pieces having sequences is aligned in a dimension of the sequences in order of the sequences that is learning data used for machine learning; and a model generating unit configured to generate the model for estimation by performing machine learning using the learning information as an input to the model for estimation performing convolution using a kernel in the dimension of the sequences.