Update documentation

_sources/retrieval/conversational_retrieval.md

@@ -50,3 +50,10 @@ Several promising research directions are emerging to address these challenges:
 - **Tool-Calling Systems**: Leveraging large language models that can call retrieval systems as tools, enabling natural language interactions while maintaining robust search capabilities through specialized retrieval components
 
 While significant challenges remain, conversational music retrieval represents an important evolution in how we interact with and discover music. Success in this area could dramatically improve the music search experience by making it more natural, contextual, and effective at serving users' diverse musical needs through sustained dialogue.
+
+
+## References
+
+```{bibliography}
+:filter: docname in docnames
+```

introduction/overview.html

@@ -463,7 +463,7 @@ <h2>Music Retrieval<a class="headerlink" href="#music-retrieval" title="Link to
 <figure class="align-default" id="retrieval">
 <img alt="../_images/retrieval.png" src="../_images/retrieval.png" />
 </figure>
-<p>Chapter 4 focuses on text-to-music retrieval, a key component in music search, detailing the task’s definition and various search methodologies. It spans from basic boolean and vector searches to advanced techniques that bridge words to music through joint embedding methods <span id="id13">[<a class="reference internal" href="../retrieval/models.html#id45" title="Jeong Choi, Jongpil Lee, Jiyoung Park, and Juhan Nam. Zero-shot learning for audio-based music classification and tagging. In ISMIR. 2019.">CLPN19</a>]</span>, addressing challenges like out-of-vocabulary terms. The chapter progresses to sentence-to-music retrieval <span id="id14">[<a class="reference internal" href="../retrieval/models.html#id56" title="Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, and Daniel PW Ellis. Mulan: a joint embedding of music audio and natural language. arXiv preprint arXiv:2208.12415, 2022.">HJL+22</a>]</span> <span id="id15">[<a class="reference internal" href="../retrieval/models.html#id55" title="Ilaria Manco, Emmanouil Benetos, Elio Quinton, and György Fazekas. Contrastive audio-language learning for music. arXiv preprint arXiv:2208.12208, 2022.">MBQF22</a>]</span> <span id="id16">[<a class="reference internal" href="../retrieval/models.html#id51" title="SeungHeon Doh, Minz Won, Keunwoo Choi, and Juhan Nam. Toward universal text-to-music retrieval. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2023.">DWCN23</a>]</span>, exploring how to integrate complex musical semantics, and conversational music retrieval for multi-turn dialog-based music retrieval <span id="id17">[<a class="reference internal" href="#id25" title="Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang, Ravi Ganti, Krisztian Balog, and Filip Radlinski. Beyond single items: exploring user preferences in item sets with the conversational playlist curation dataset. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023.">CLZ+23</a>]</span>. It introduces evaluation metrics and includes practical coding exercises for developing a basic joint embedding model for music search. This chapter focuses on how models address <code class="docutils literal notranslate"><span class="pre">users'</span> <span class="pre">musical</span> <span class="pre">queries</span></code> in various ways.</p>
+<p>Chapter 4 focuses on text-to-music retrieval, a key component in music search, detailing the task’s definition and various search methodologies. It spans from basic boolean and vector searches to advanced techniques that bridge words to music through joint embedding methods <span id="id13">[<a class="reference internal" href="../retrieval/models.html#id45" title="Jeong Choi, Jongpil Lee, Jiyoung Park, and Juhan Nam. Zero-shot learning for audio-based music classification and tagging. In ISMIR. 2019.">CLPN19</a>]</span>, addressing challenges like out-of-vocabulary terms. The chapter progresses to sentence-to-music retrieval <span id="id14">[<a class="reference internal" href="../retrieval/models.html#id56" title="Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, and Daniel PW Ellis. Mulan: a joint embedding of music audio and natural language. arXiv preprint arXiv:2208.12415, 2022.">HJL+22</a>]</span> <span id="id15">[<a class="reference internal" href="../retrieval/models.html#id55" title="Ilaria Manco, Emmanouil Benetos, Elio Quinton, and György Fazekas. Contrastive audio-language learning for music. arXiv preprint arXiv:2208.12208, 2022.">MBQF22</a>]</span> <span id="id16">[<a class="reference internal" href="../retrieval/models.html#id51" title="SeungHeon Doh, Minz Won, Keunwoo Choi, and Juhan Nam. Toward universal text-to-music retrieval. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2023.">DWCN23</a>]</span>, exploring how to integrate complex musical semantics, and conversational music retrieval for multi-turn dialog-based music retrieval <span id="id17">[<a class="reference internal" href="../retrieval/conversational_retrieval.html#id6" title="Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang, Ravi Ganti, Krisztian Balog, and Filip Radlinski. Beyond single items: exploring user preferences in item sets with the conversational playlist curation dataset. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023.">CLZ+23</a>]</span>. It introduces evaluation metrics and includes practical coding exercises for developing a basic joint embedding model for music search. This chapter focuses on how models address <code class="docutils literal notranslate"><span class="pre">users'</span> <span class="pre">musical</span> <span class="pre">queries</span></code> in various ways.</p>
 </section>
 <section id="music-generation">
 <h2>Music Generation<a class="headerlink" href="#music-generation" title="Link to this heading">#</a></h2>