音樂能讓人表達情緒（Juslin & Laukka, 2004）、抒發心情。在網路越趨發達及線上音樂快速崛起的環境下，聆聽線上音樂成了現代人重要的休閒活動之一。然而線上音樂選擇眾多，如何幫助使用者找到符合期望的音樂，以滿足其音樂資訊需求即為重要的課題。傳統的音樂詮釋、分類方式（如：歌手、歌名、音樂類型等等）無法針對特定情境、情緒及使用目的，來滿足使用者的未知音樂資訊需求，也無法讓使用者享有音樂探索的樂趣。本研究認為線上音樂檢索系統除了提供傳統的音樂描述之外，還應包含以使用者為中心的各種音樂描述屬性。因此本研究旨在找出所有以使用者為中心的潛在音樂描述屬性，研究設計上從音樂感知和特定情境之下的未知音樂資訊需求等兩個面向來蒐集描述內容，再採以內容分析、實驗及訪談等方法，蒐集和分析使用者之音樂及音樂資訊需求描述內容。最後進行歸納和分類，建構完整的音樂描述屬性架構。
為取得使用者之未知音樂資訊需求描述，本研究從KKBOX以及PTT之音樂討論區中，立意取樣500則包含未知音樂資訊需求之提問進行內容分析。研究結果顯示，線上音樂提問中的描述屬性主要包含48個屬性，可歸納為「音樂描述資訊」、「音樂相關資訊」、「舉例參考」、「使用者資訊」、「音樂相關情境」、「音樂認知」等六大類別。研究發現除了客觀描述之外，使用者利用大量的認知、感受、想法或是與音樂相關的情境等主觀詞彙來幫助描述未知音樂資訊需求。
此外，本研究一共邀請了24位使用者進行音樂描述實驗，以蒐集使用者之音樂描述資料。實驗除了「音樂描述任務」，另外設計了「音樂檢索描述」、「音樂畫面描述」和「音樂情境描述」等三個任務，以找出多種描述情境之下的潛在描述屬性，並輔以訪談來了解使用者描述的內在意涵。最後以內容分析法整理上述資料內容。研究結果發現從四個任務中分別包含不同層面的音樂認知特性，全部音樂描述中一共包含74個屬性，可歸納為「音樂描述資訊」、「音樂相關資訊」、「舉例參考」、「使用者資訊」、「音樂相關情境」、「音樂認知」和「視覺元素」等七大類別，並發現視覺描述有助於音樂感知和描述。
整體而言，使用者對於音樂需求或音樂本身的認知及描述方式相當廣泛且多元，除了包含大量的聯想之外，還與使用者本身具有高度的連結性；另外，音樂聆聽或使用情境對使用者的描述有重要的影響。
根據上述研究結果，本研究提出以下建議：建議提供完整、多元的音樂描述方式，讓使用者能依照特定情境、情緒或使用目的來找尋音樂，例如事件、場合、情緒、抽象形容、作用等主觀描述屬性；對於音樂推薦服務，建議增加多重條件的音樂篩選模式，讓使用者能夠交叉查詢的方式滿足特殊的音樂需求；對於音樂探索，則建議針對「視覺畫面」和「聯想物」等跳脫音樂層次的描述概念，提供瀏覽或是圖像替代的描述方式，增加使用者和音樂偶遇的機會，以幫助使用者從中得到更佳的音樂探索經驗。

Music can let people express their emotions (Juslin & Laukka, 2004) and also their feelings. Under the rapid rise of Internet and online music, listening to online music has become one of everyone's major leisure activities. How to help users find the music which meet their music information needs is an important issue. Most of the online music retrieval systems use the traditional musical bibliographic metadata (such as artist, song title, genre, etc.) to describe and classify music collection. However, such classification doesn't meet users' unknown music information needs. This study suggests that in addition to traditional music attributes, music retrieval system should also provide a variety of user-centered music attributes.
This study collects 500 online music queries of unknown music information needs from the the KKBOX and PTT music forum. By doing content analysis, we found 48 attributes from the music queries which can be summarized as 6 categories. It shows that users use a lot of subjective descriptions with music cognition, feelings, ideas and contexts in addition to objective descriptions to help describe the unknown music information needs.
In addition, this study invited a total of 24 users to join the music describing experiment to collect their music descriptions. The experiment includes four different tasks to explore all potential music attributes from a variety of contexts. All information gathered from tasks will be analyzed through Content analysis. The results show that there are a total of 74 attributes and can be summarized as 7 categories. We can see different phases of music characteristics. Furthermore, the results show that visual description can help music recognition and description.
Overall, the users’ cognition and description of music and music information needs are extensive and diverse. The descriptions are full of association and high connection with users. Moreover, contexts of music listening and uses have great impact on users' music description.
Based on the findings, this study offer the following recommendations: music retrieval systems should provide complete and diverse music attributes, allowing users to find the music in accordance with the specific context, mood or purpose of use; for better music recommendation service, it should provide multiple attributes choices, allowing users to cross search the music; for better music discovery service, it is recommended to provide attributes like "music images" and "association objects" to increase the opportunities of music encountering. At last, all recommendations are to help users have better music discovery experiences.

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