Problem-Solving

Audio Volume Issues in Audio File Processing

When producing and distributing audio content, do you face these problems?

1. Volume Inconsistency Issues

• Volume levels are not unified across multiple audio files
• Volume differences occur due to different recording environments and equipment
• Listeners need to frequently adjust volume levels

2. Quality Inconsistency Issues

• Noise and unwanted frequencies are mixed in
• Silent sections are too long and difficult to listen to
• Unable to achieve professional-quality audio

3. Manual Processing Limitations

• Processing large numbers of audio files individually is inefficient
• Automation is difficult with GUI audio editing software
• Applying consistent processing standards is challenging

Real-world Audio Quality Challenge Cases

Failure Case: Limitations of Manual Adjustment

# Traditional approach
# 1. Open each file in audio editing software
# 2. Visually and auditorily adjust levels
# 3. Manually apply noise reduction
# 4. Manually cut silent sections

# Problems:
# - Time-consuming for processing large numbers of files
# - Processing standards are subjective and inconsistent
# - Quality variations due to human errors

The solution to this problem is automated volume normalization with ffmpeg.

Challenges in Japanese Text Generation

When working on Japanese text summarization, title generation, and document classification tasks, do you face these problems?

1. Accuracy Issues

• Traditional rule-based methods cannot generate natural Japanese text
• English-oriented models cannot handle Japanese grammar and expressions
• Need to build separate models for multiple tasks

2. Development Cost Issues

• Time and resources required for task-specific model development
• Different approaches needed for document classification, summarization, and title generation
• Enormous effort required for preparing training data and building models

3. Operational Complexity

• Need to manage and operate multiple models
• Different APIs and interfaces for each task
• Complex model updates and maintenance

Real-world Text Generation Challenge Cases

Failure Case: Limitations of Task-specific Individual Development

# Traditional approach
classification_model = load_bert_classifier()      # For document classification
summarization_model = load_summarization_model()   # For summarization
title_generation_model = load_title_model()        # For title generation

# Problems:
# - Managing 3 separate models
# - 3x memory usage
# - High development and maintenance costs

The solution to this problem is Japanese T5 (Text-To-Text Transfer Transformer).

問題：なぜキーワード検索では欲しい文書が見つからないのか

社内の膨大な文書データベースから「機械学習の性能向上に関する文書」を探しているとします。キーワード検索で「機械学習」「性能向上」と入力しても、本当に必要な文書が見つからない経験はありませんか？

問題：なぜ実際のプロジェクトでは十分なデータが集まらないのか

「ニュース記事を『政治・経済・スポーツ・エンタメ』に自動分類したい。でも各カテゴリ30件ずつしかラベル付きデータがない…」

機械学習の教科書では数万件のデータセットが当たり前ですが、実際のビジネス現場では全く異なります。

問題：なぜ日本語のテキスト処理は難しいのか

「英語のNLPライブラリを日本語に適用したら、全然うまくいかない…」

日本語の自然言語処理は、英語中心に設計されたツールでは対応できない独特な課題が多数存在します。

問題：なぜ機械学習の実験結果が再現できないのか

「先週良い結果が出たモデルがあったんだけど、どのパラメータを使ったか覚えてる？」

機械学習プロジェクトでこんな会話を聞いたことはありませんか？多くのチームが直面する典型的な問題です。