目录

1. 中文翻译：
2. 英文原文：
3. D题数据下载：

中文翻译：

问题D：音乐的影响

自古以来，音乐就已成为人类社会的一部分，已成为文化遗产的重要组成部分。为了理解音乐在人类集体经验中所扮演的角色，我们被要求开发一种量化音乐发展的方法。在创作新音乐时，有许多因素会影响艺术家，包括其天赋的创造力，当前的社会或政治事件，使用新乐器或工具的机会或其他个人经历。我们的目标是了解和衡量先前制作的音乐对新音乐和音乐艺术家的影响。

一些艺术家可以列出十几个或更多他们认为对自己的音乐作品有影响的艺术家。还建议可以通过歌曲特征（例如结构，节奏或歌词）之间的相似程度来衡量影响力。音乐有时会发生革命性的变化，提供新的声音或节奏，例如何时出现新的流派，或者对现有流派（例如古典，流行/摇滚，爵士等）进行重新发明。这可能是由于一系列小变化，艺术家的合作努力，一系列有影响力的艺术家或社会内部的变化所致。

许多歌曲具有相似的声音，许多艺术家为音乐类型的重大转变做出了贡献。有时，这些变化是由于一位艺术家影响了另一位艺术家。有时，这是对外部事件（例如重大世界事件或技术进步）的响应而出现的变化。通过考虑歌曲的网络及其音乐特征，我们可以开始捕捉音乐艺术家之间的相互影响。而且，也许，我们还可以更好地了解音乐随着时间的流逝在整个社会中的发展。

集成集体音乐协会（ICM）指定了您的团队，来开发一种衡量音乐影响力的模型。这个问题要求您探求艺术家和流派的进化和革命趋势。为此，ICM为您的团队提供了一些数据集：

1. “influence_data”代表艺术家自己报告的音乐影响者和追随者，以及行业专家的意见。这些数据包含过去90年中5,854位艺术家的影响者和关注者。

2. “full_music_data”提供了16个变量项，包括音乐特征（如舞蹈性，速度，响度和音律），以及98,340首歌曲中的每一个的artist_name（艺术家名字）和artist_id（艺术家id）。这些数据用于创建两个摘要数据集，包括：

   a.按照艺术家的平均值“data_by_artist”
   b.按照年份的平均值“data_by_year”

注意：这些文件中提供的数据是较大数据集的子集。这些文件包含您应为该问题使用的唯一数据。

为了执行这个具有挑战性的项目，ICM协会要求您的团队通过以下措施，通过音乐艺术家随时间的影响来探索音乐的发展：

• 使用influence_data数据集或其中的一部分来创建音乐影响力的（多个）定向网络，将影响者连接到追随者。开发可捕获此网络中“音乐影响力”的参数。通过创建定向影响者网络的子网来探索音乐影响力的子集。描述此子网。您的“音乐影响力”措施在此子网络中体现了什么？

• 使用音乐特征的full_music_data和/或两个摘要数据集（包括艺术家和年份）来制定音乐相似度的度量。使用您的度量，流派内的艺术家是否比流派间的艺术家更相似？

• 比较流派之间和流派之间的相似性和影响。什么是流派的区别，流派如何随时间变化？有些类型与其他类型有关吗？

• 指示data_influence数据集中报告的相似性数据是否表明所标识的影响者实际上在影响相应的艺术家。 “影响者”实际上会影响追随者创作的音乐吗？是某些音乐特征比其他音乐特征更具“感染力”，或者它们在影响特定艺术家的音乐方面起着相似的作用？

• 从这些数据中确定是否存在可能标志着音乐发展中的革命（重大飞跃）的特征？在您的网络中，哪些艺术家代表着革命者（重大变革的影响者）？

• 分析一种类型音乐随时间变化的影响过程。您的团队能否确定能揭示动态影响者的指标，并解释流派或艺术家随时间的变化？

• 您的作品如何表达有关音乐在时间或环境方面的文化影响的信息？或者，如何在网络中识别社会，政治或技术变化（例如互联网）的影响？

向ICM协会写一份一页纸的文件，说明使用您的方法通过网络理解音乐影响的价值。考虑到这两个问题数据集仅限于某些类型，然后又针对这两个数据集共有的艺术家，您的作品或解决方案将如何随着更多或更丰富的数据而发生变化？建议进一步研究音乐及其对文化的影响。

来自音乐，历史，社会科学，技术和数学领域的跨学科，多元化的ICM协会期待您的最终报告。

您的PDF解决方案（总共不超过25页）应包括：

• 一页的摘要表。
• 目录。
• 您的完整解决方案。
• 对ICM协会的一页文件。
• 参考文献清单。

注意：2021年的新要求！现在，ICM竞赛限制为25页。提交的所有方面均计为25页的限制：摘要表，目录，解决方案主体，图像和表格，一页文档，参考列表和任何附录。

附件
针对此问题，我们提供了以下四个数据文件。提供的数据文件包含您应用于此问题的唯一数据。

1. influence_data.csv
2. full_music_data.csv
3. data_by_artist.csv
4. data_by_year.csv

数据描述

1. influence_data.csv（数据以utf-8编码，以允许处理特殊字符）

   • influence_id：给列出为影响者的人的唯一编号。 （数字字符串）
   • influence_name：影响者的名称，由关注者或行业专家提供。 （串）
   • influence_main_genre：最能描述有影响力的艺术家创作的大部分音乐的流派。 （如果有）（字符串）
   • influence_active_start：影响力艺术家开始其音乐事业的十年。 （整数）
   • follower_id：提供给列出为关注者的艺术家的唯一编号。 （数字字符串）
   • follower_name：跟随有影响力的艺术家的艺术家的名字。 （串）
   • follower_main_genre：最能描述以下艺术家创作的大部分音乐的流派。 （如果有）（字符串）
   • follower_active_start：以下艺术家开始音乐生涯的十年。 （整数）

2. full_music_data.csv

3. data_by_artist.csv
4. data_by_year.csv

“full_music_data”, “data_by_artist”, “data_by_year”三个表中的音乐特征：

• artist_name: 创造歌曲的艺术家。（数组）
• artist_id: 和influence_data.csv文件中相同的艺术家唯一编号。 （数字字符串）

音乐特征：

• danceability: 根据节奏，节奏稳定性，拍子强度和整体规律性等音乐元素的组合来衡量轨道适合跳舞的方式。值0.0为最低可跳舞能力，而1.0为最高可跳舞能力。 （浮点数）
• energy: 表示对强度和活动的感知的量度。值0.0为最小强度/能量，而1.0为强度最大/能量。通常，充满活力的曲目会让人感到快速，响亮且嘈杂。例如，死亡金属具有较高的能量，而巴赫前奏的得分则较低。有助于此属性的感知特征包括动态范围，感知的响度，音色，发作率和一般熵。 （浮点数）
• valence: 一种描述曲目传达的音乐积极性的量度。值0.0最消极，1.0最积极。价态高的音轨听起来更积极（例如，快乐，开朗，欣快），而价态低的音轨听起来更负面（例如，悲伤，沮丧，愤怒）。 （浮点数）
• tempo: 曲目的总体估计拍速，以每分钟拍数（BPM）为单位。用音乐术语来说，节奏是指给定乐曲的速度或节奏，它直接来自平均拍子持续时间。 （浮点数）
• loudness: 轨道的整体响度，以分贝（dB）为单位。值的典型范围是-60至0 db。响度值是整个轨道的平均值，可用于比较轨道的相对响度。响度是声音的质量，它是身体力量（振幅）的主要心理关联。 （浮点数）
• mode: 音轨的模态（主要或次要）的指示，其旋律内容所源自的音阶类型。 Major用1表示，minor用0表示。
• key: 曲目的估计总体密钥。整数使用标准音高类别符号映射到音高。例如。 0 = C，1 =C♯/ D♭，2 = D，依此类推。如果未检测到密钥，则密钥的值为-1。 （整数）

人声类型

• acousticness: 磁道是否是声学的置信度度量（不增强技术或增强电性能）。值1.0表示轨道是声学的高置信度。 （浮点数）
• instrumentalness: 预测曲目是否不包含人声。在这种情况下，“哦”和“啊”的声音被视为乐器。说唱或口语单词轨迹显然是“声音”。器乐性值越接近1.0，则曲目中没有人声内容的可能性越大。高于0.5的值旨在表示乐器音轨，但随着该值接近1.0，置信度更高。 （浮点数）
• liveness: 检测曲目中观众的存在。较高的活跃度值表示增加了实时执行轨道的可能性。高于0.8的值很可能会显示该轨道处于活动状态。 （浮点数）
• speechiness: 检测曲目中口语的存在。与录音类似的语音内容（例如脱口秀，有声读物，诗歌）越多，属性值就越接近1.0。大于0.66的值描述的曲目可能完全由口语组成。介于0.33到0.66之间的值描述了可能同时包含音乐和语音的曲目，无论是分段还是分层的（包括说唱音乐）。低于0.33的值最有可能代表音乐和其他非语音类曲目。 （浮点数）
• explicit: 检测曲目中的脏话（true（1）=有； false（0）= 没有，或者未知）。 （布尔值）

描述

• duration_ms: 轨道的持续时间（以毫秒为单位）。 （整数）
• popularity: 这首歌的受欢迎程度。该值将在0到100之间，其中100是最受欢迎的值。受欢迎程度是通过算法计算的，并且在很大程度上取决于音轨的总播放次数以及这些播放的最近时间。一般而言，现在播放频率更高的歌曲将比过去播放频率更高的歌曲具有更高的知名度。重复曲目（例如，同一首曲目和一张专辑中的同一曲目）将被独立评估。艺术家和专辑的流行度是从曲目流行度中数学得出的。 （整数）
• year: 发行曲目的年份。 （1921年至2020年的整数）
• release_date: 曲目发布的日历日期，大多数采用yyyy-mm-dd格式，但是日期的精度可能会有所不同，有些只是以yyyy给出。
• song_title (censored): 曲目的名称。 （字符串）已运行软件以删除歌曲标题中的任何潜在脏话单词。
• count: full_music_data.csv文件中表示特定艺术家的歌曲数。 （整数）

英文原文：

2021 ICM
Problem D: The Influence of Music

Music has been part of human societies since the beginning of time as an essential component of cultural heritage. As part of an effort to understand the role music has played in the collective human experience, we have been asked to develop a method to quantify musical evolution. There are many factors that can influence artists when they create a new piece of music, including their innate ingenuity, current social or political events, access to new instruments or tools, or other personal experiences. Our goal is to understand and measure the influence of previously produced music on new music and musical artists.

Some artists can list a dozen or more other artists who they say influenced their own musical work. It has also been suggested that influence can be measured by the degree of similarity between song characteristics, such as structure, rhythm, or lyrics. There are sometimes revolutionary shifts in music, offering new sounds or tempos, such as when a new genre emerges, or there is a reinvention of an existing genre (e.g. classical, pop/rock, jazz, etc.). This can be due to a sequence of small changes, a cooperative effort of artists, a series of influential artists, or a shift within society.

Many songs have similar sounds, and many artists have contributed to major shifts in a musical genre. Sometimes these shifts are due to one artist influencing another. Sometimes it is a change that emerges in response to external events (such as major world events or technological advances). By considering networks of songs and their musical characteristics, we can begin to capture the influence that musical artists have on each other. And, perhaps, we can also gain a better understanding of how music evolves through societies over time.

Your team has been identified by the Integrative Collective Music (ICM) Society to develop a model that measures musical influence. This problem asks you to examine evolutionary and revolutionary trends of artists and genres. To do this, your team has been given several data sets by the ICM:
1) “influence_data” represents musical influencers and followers, as reported by the artists themselves, as well as the opinions of industry experts. These data contains influencers and followers for 5,854 artists in the last 90 years.
2) “full_music_data” provides 16 variable entries, including musical features such as danceability, tempo, loudness, and key, along with artist_name and artist_id for each of 98,340 songs. These data are used to create two summary data sets, including:
a. mean values by artist “data_by_artist”,
b. means across years “data_by_year”.

Note: DATA provided in these files are a subset of larger data sets. These files CONTAIN THE ONLY DATA YOU SHOULD USE FOR THIS PROBLEM.

To carry out this challenging project, the ICM Society asks your teams to explore the evolution of music through the influence across musical artists over time, by doing the following:

• Use the influence_data data set or portions of it to create a (multiple) directed network(s) of musical influence, where influencers are connected to followers. Develop parameters that capture ‘music influence’ in this network. Explore a subset of musical influence by creating a subnetwork of your directed influencer network. Describe this subnetwork. What do your ‘music influence’ measures reveal in this subnetwork?
• Use full_music_data and/or the two summary data sets (with artists and years) of music characteristics, to develop measures of music similarity. Using your measure, are artists within genre more similar than artists between genres?
• Compare similarities and influences between and within genres. What distinguishes a genre and how do genres change over time? Are some genres related to others?
• Indicate whether the similarity data, as reported in the data_influence data set, suggest that the identified influencers in fact influence the respective artists. Do the ‘influencers’ actually affect the music created by the followers? Are some music characteristics more ‘contagious’ than others, or do they all have similar roles in influencing a particular artist’s music?
• Identify if there are characteristics that might signify revolutions (major leaps) in musical evolution from these data? What artists represent revolutionaries (influencers of major change) in your network?
• Analyze the influence processes of musical evolution that occurred over time in one genre. Can your team identify indicators that reveal the dynamic influencers, and explain how the genre(s) or artist(s) changed over time?
• How does your work express information about cultural influence of music in time or circumstances? Alternatively, how can the effects of social, political or technological changes (such as the internet) be identified within the network?

Write a one-page document to the ICM Society about the value of using your approach to understanding the influence of music through networks. Considering the two problem data sets were limited to only some genres, and subsequently to those artists common to both data sets, how would your work or solutions change with more or richer data? Recommend further study of music and its effect on culture.

The ICM Society, an interdisciplinary and diverse group from the fields of music, history, social science, technology, and mathematics, looks forward to your final report.

Your PDF solution of no more than 25 total pages should include:

• One-page Summary Sheet.
• Table of Contents.
• Your complete solution.
• One-page document to ICM society.
• References list.

Note: New for 2021! The ICM Contest now has a 25-page limit. All aspects of your submission count toward the 25-page limit: Summary Sheet, Table of Contents, Main Body of Solution, Images and Tables, One-page Document, Reference List, and any Appendices.

Attachments
We provide the following four data files for this problem. THE DATA FILES PROVIDED CONTAIN THE ONLY DATA YOU SHOULD USE FOR THIS PROBLEM.

1. influence_data.csv
2. full_music_data.csv
3. data_by_artist.csv
4. data_by_year.csv

Data Descriptions

1. influence_data.csv (Data is encoded in utf-8 to allow for handling of special characters):
   • influencer_id: A unique identification number given to the person listed as influencer. (string of digits)
   • influencer_name: The name of the influencing artist as given by the follower or industry experts. (string)
   • influencer_main_genre: The genre that best describes the bulk of the music produced by the influencing artist. (if available) (string)
   • influencer_active_start: The decade that the influencing artist began their music career. (integer)
   • follower_id: A unique identification number given to the artist listed as follower. (string of digits)
   • follower_name: The name of the artist following an influencing artist. (string)
   • follower_main_genre: The genre that best describes the bulk of the music produced by the following artist. (if available) (string)
   • follower_active_start: The decade that the following artist began their music career. (integer)
2. full_music_data.csv
3. data_by_artist.csv
4. data_by_year.csv

Spotify audio features from the “full_music_data”, “data_by_artist”, “data_by_year”:

• artist_name: The artist who performed the track. (array)
• artist_id: The same unique identification number given in the influence_data.csv file. (string of digits)

Characteristics of the music:

• danceability: A measure of how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. (float)
• energy: A measure representing a perception of intensity and activity. A value of 0.0 is least intense/energetic and 1.0 is most intense/energetic. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. (float)
• valence: A measure describing the musical positiveness conveyed by a track. A value of 0.0 is most negative and 1.0 is most positive. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). (float)
• tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. (float)
• loudness: The overall loudness of a track in decibels (dB). Values typical range between -60 and 0 db. Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). (float)
• mode: An indication of modality (major or minor), the type of scale from which its melodic content is derived, of a track. Major is represented by 1 and minor is 0.
• key: The estimated overall key of the track. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value for key is -1. (integer)

Type of vocals:

• acousticness: A confidence measure of whether the track is acoustic (without technology enhancements or electrical amplification). A value of 1.0 represents high confidence the track is acoustic. (float)
• instrumentalness: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. (float)
• liveness: Detects the presence of an audience in a track. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. (float)
• speechiness: Detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. (float)
• explicit: Detects explicit lyrics in a track (true (1) = yes it does; false (0) = no it does not OR unknown). (Boolean)`

Description:

• duration_ms: The duration of the track in milliseconds. (integer)
• popularity: The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Generally speaking, songs that are being played more frequently now will have a higher popularity than songs that were played more frequently in the past. Duplicate tracks (e.g. the same track from a single and an album) are rated independently. Artist and album popularity are derived mathematically from track popularity. (integer)
• year: The year of release of a track. (integer from 1921 to 2020)
• release_date: The calendar date of release of a track mostly in yyyy-mm-dd format, however precision of date may vary and some just given as yyyy.
• song_title (censored): The name of the track. (string) Software was run to remove any potential explicit words in the song title.
• count: The number of songs a particular artist is represented in the full_music_data.csv file. (integer)

D题数据下载：

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