Cuepoint Determination Using CNN for Media Transitions
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Solution Overview
Problem
Current music software solutions lack automation and accuracy in cuepoint identification for smooth song transitions, especially in scenarios where both songs are unknown, relying heavily on human expertise and being limited to specific song pairs.
Innovation Solution
A cuepoint determination system utilizing a convolutional neural network (CNN) to predict candidate cuepoint placements by normalizing audio content into beats, partitioning them into temporal sections, extracting acoustic features, and determining cuepoint placements independently from other media content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual cuepoint identification by expert humans is used, then accuracy in cuepoint placement is improved, but productivity and ease of operation deteriorate due to requiring human expertise and time
Solution Approach 1:
The system performs automatic cuepoint identification without requiring human intervention. The CNN model processes audio content independently, normalizing it into beats, extracting acoustic features, and determining cuepoint placements autonomously, thereby eliminating the need for manual expert analysis while maintaining high accuracy
Solution Approach 2:
The patent replaces the mechanical process of manual human analysis with an automated computational system. The CNN-based automated system substitutes human experts, using machine learning to analyze audio patterns and identify cuepoints, thus improving productivity while maintaining measurement precision
2Productivity
If current automated solutions are used, then productivity is improved, but measurement precision deteriorates due to lack of accuracy in cuepoint identification
Solution Approach 1:
The patent employs a CNN-based automated system that replaces inadequate previous automation methods. The system processes audio content through multiple stages including normalization into beats, temporal sectioning, acoustic feature extraction, and CNN-based prediction, achieving both high productivity and accurate cuepoint identification
Solution Approach 2:
The system transforms the audio content through multiple parameter transformations: converting raw audio into beat-normalized representations, dividing into temporal sections, extracting acoustic features, and finally predicting cuepoints. These parameter changes enable the system to achieve high accuracy while maintaining full automation
3Ease of operation
If transition automation is implemented, then ease of operation is improved, but adaptability deteriorates when both songs in the transition are unknown
Solution Approach 1:
The system is designed to handle multiple scenarios universally: it can process transitions between known songs, unknown songs, and shuffled playlists. The CNN model analyzes audio content independently of song identification, making the system adaptable to any pair of songs regardless of whether they are known or unknown, thereby maintaining both ease of operation and adaptability
4Measurement precision
If complex audio analysis is performed to improve cuepoint accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the audio analysis process into distinct modular stages: normalization into beats, partitioning into temporal sections, extraction of acoustic features, and CNN-based prediction. This segmentation allows complex analysis to be performed in manageable steps, improving accuracy while organizing system complexity into manageable modules
Data Source
AI summary
A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.