Smartphone Audio Editing With Track-Aware Sound Quality Menus
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Solution Overview
Problem
Conventional recording and editing applications for music content production on portable devices lack convenience in managing sound quality improvement settings, requiring users to manually adjust settings based on track type, which can lead to incorrect designations.
Innovation Solution
An information processing apparatus and method that automatically displays sound quality improvement menus based on the type of sound source data, utilizing a deep neural network model for AI processing to enhance sound quality, allowing users to produce music content with improved convenience by automating setting adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually adjust sound quality improvement settings based on track type, then users can customize processing for different sound sources, but users may make incorrect designations and the operation becomes complex
Solution Approach 1:
The system automatically identifies the track type and displays only the corresponding sound quality improvement menu items without requiring user input for classification. The apparatus serves itself by autonomously determining which processing options are appropriate for each sound source data, eliminating manual setting adjustment while maintaining accuracy.
Solution Approach 2:
The sound quality improvement menu is customized and displayed differently for each track type (vocal, instrument, etc.). Each menu contains only the processing items relevant to that specific sound source type, providing localized and context-appropriate options rather than a universal menu for all tracks.
2Adaptability or versatility
If the system displays all sound quality improvement menu items for all track types, then users have access to all processing options, but the menu becomes complex and confusing
Solution Approach 1:
The menu structure is optimized for each track type by displaying only the processing items applicable to that specific type. Vocal tracks show vocal-specific processing options, instrument tracks show instrument-specific options, and so on. This maintains versatility within each context while eliminating the complexity of a comprehensive universal menu.
Solution Approach 2:
The sound quality improvement menu is segmented into multiple track-type-specific menus rather than presenting a single comprehensive menu. Each segment contains only the processing options relevant to its designated track type, making the overall system more manageable and easier to navigate despite covering diverse processing capabilities.
3Ease of operation
If the system automatically identifies track type and displays corresponding menu, then operation becomes simpler, but the system complexity increases
Solution Approach 1:
The system automatically performs track type identification and menu configuration without requiring user intervention. The apparatus autonomously analyzes the sound source data characteristics, determines the appropriate track type, and displays the corresponding processing menu, making the complex identification process transparent to the user.
Solution Approach 2:
The system introduces an automatic track type identification function as an intermediary between the raw sound source data and the sound quality improvement menu. This intermediary layer automatically classifies the track type and translates it into the appropriate menu configuration, shielding users from the complexity of the classification process while enabling simplified operation.
Data Source
AI summary
A smartphone (corresponding to an example of an “information processing apparatus”) includes: an application execution unit provided so as to be able to execute a recording editing application (corresponding to an example of an “application having a recording function and an editing function of sound source data”); and a display control unit that causes a display unit to variably display a menu for sound quality improvement in conjunction with a type of the sound source data, the sound source data being selected by a user as a target for the sound quality improvement via the recording editing application.