DOCSLIB.ORG

Effects of Album and Artist Filters in Audio Similarity Computed for Very

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Effects of Album and Artist Filters in Audio Similarity Computed for Very Arthur Flexer∗ and Dominik Schnitzer∗† Effects of Album and Artist ∗Austrian Research Institute for Artificial Filters in Audio Similarity Intelligence Freyung 6/6 A-1010 Vienna, Austria Computed for Very Large [email protected] †Department of Computational Music Databases Perception Johannes Kepler University Linz Altenberger Str. 69 A-4040 Linz, Austria [email protected] In music information retrieval, one of the classification results quite considerably (as much as central goals is to automatically recommend from 71 percent down to 27 percent, for one of their music to users based on a query song or query music collections). These over-optimistic accuracy artist. This can be done using expert knowl- results due to not using an artist filter have been edge (e.g., www.pandora.com), social meta-data confirmed in other studies (Flexer 2006; Pampalk (e.g., www.last.fm), collaborative filtering (e.g., 2006). Other results suggest that the use of an artist www.amazon.com/mp3), or by extracting informa- filter not only lowers genre classification accuracy tion directly from the audio (e.g., www.muffin.com). but may also erode the differences in accuracies In audio-based music recommendation, a well- between different techniques (Flexer 2007). known effect is the dominance of songs from the All these results were achieved on rather small same artist as the query song in recommendation databases (from 700 to 15,000 songs). Often whole lists. albums from an artist were part of the database, This effect has been studied mainly in the perhaps even more than one. These specifics of context of genre-classification experiments. Because the databases are often unclear and not properly no ground truth with respect to music similarity documented. The present article extends these usually exists, genre classification is widely used for results by analyzing a very large data set (over evaluation of music similarity. Each song is labelled 250,000 songs) containing multiple albums from as belonging to a music genre using, e.g., advice individual artists. We try to answer the following of a music expert. High genre classification results questions: indicate good similarity measures. If, in genre classification experiments, songs from the same 1. Is there an album and artist effect even in artist are allowed in both training and test sets, this very large databases? can lead to over-optimistic results since usually all 2. Is the album effect larger than the artist songs from an artist have the same genre label. It effect? can be argued that in such a scenario one is doing 3. What is the influence of database size on artist classification rather than genre classification. music recommendation and classification? One could even speculate that the specific sound of As will be seen, we find that the artist effect does an album (mastering and production effects) is being exist in very large databases, and the album effect is classified. In Pampalk, Flexer, and Widmer (2005) bigger than the artist effect. the use of a so-called “artist filter” that ensures that a given artist’s songs are either all in the training set, or all in the test set, is proposed. Those authors found Data that the use of such an artist filter can lower the For our experiments we used a data set D(AL L) Computer Music Journal, 34:3, pp. 20–28, Fall 2010 of S = 254,398 song excerpts (30 seconds each) c 2010 Massachusetts Institute of Technology. from a popular Web store selling music. The freely 20 Computer Music Journal Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/COMJ_a_00004 by guest on 24 September 2021 Figure 1. Percentages Figure 2. Percentages (y-axis) of albums having (y-axis) of artists having 6, 1, 2, 3, 4, or 5 to 8 genre 7, ..., 19, or 20 to 29 labels (x-axis). albums (x-axis). 30 25 20 20 10 10 0 0 1 2 3 4 5 to 8 6 7 8 9 10 11 12 13 14 15 16 17 18 19 >20 Table 1. Percentages of Songs Belonging to the parsed automatically from the HTML code. The 22 Genres, with Multiple Membership excerpts are from U = 18,386 albums from A = 1,700 Allowed artists. From the 280 existing different hierarchical Genre Percentage genres, only the G = 22 general ones on top of the hierarchy are being kept for further analysis (e.g., Pop 49.79 “Pop/General” is kept but not “Pop/Vocal Pop”). Classical 12.89 The names of the genres plus percentages of songs Broadway 7.45 belonging to each of the genres are given in Table 1. Soundtracks 1.00 (Each song is allowed to belong to more than one Christian/Gospel 10.20 New Age 2.48 genre, hence the percentages in Table 1 add up to Miscellaneous 6.11 more than 100 percent.) The genre information is Opera/Vocal 3.24 identical for all songs on an album. The numbers Alternative Rock 27.13 of genre labels per album are given in Figure 1. Our Rock 51.78 database was set up so that every artist contributes Rap/Hip-Hop 0.98 between 6 and 29 albums (see Figure 2). R&B 4.26 To study the influence of the size of the database Hard Rock/Metal 15.85 on results, we created random non-overlapping Classic Rock 15.95 splits of the entire data set: D(1/2) is two data sets Country 4.07 with the mean number of song excerpts = 127,199; Jazz 6.98 D(1/20) is twenty data sets with the mean number Children’s Music 7.78 = D / International 9.69 of songs excerpts 12,719.9; and (1 100) is one Latin Music 0.54 hundred data sets with the mean number of songs = Folk 11.18 excerpts 2,543.98. An artist with all their albums Dance & DJ 5.24 is always a member of a single data set. Blues 11.24 Methods available preview song excerpts were obtained with an automated Web-crawl. All meta-information We compare two approaches based on different pa- (artist name, album title, song title, genres) is rameterizations of the data. Whereas mel-frequency Flexer and Schnitzer 21 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/COMJ_a_00004 by guest on 24 September 2021 cepstral coefficients (MFCCs) are a relatively di- as (Penny 2001): rect representation of the spectral information 1 det ( ) of a signal and therefore of the specific “sound” KL pq = p + Tr −1 N( ) log p q or “timbre” of a song, fluctuation patterns (FPs) 2 det ( q) are a more abstract kind of feature describing the + μ − μ −1 μ − μ − amplitude modulation of the loudness per fre- ( p q) p ( q p) d (1) quency band. It is our hypothesis that MFCCs are more prone to pick up production and master- where Tr(M) denotes the trace of the matrix M, ing effects of a single album as well as the spe- Tr(M) = i=1..nmi,i. The divergence is symmetrized cific “sound” of an artist (voice, instrumentation, by computing: etc.). KL (pq) + KL (qp) KL = N N (2) sym 2 Mel-Frequency Cepstral Coefficients and Single Gaussians (G1) Fluctuation Patterns and Euclidean Distance (FP) We use the following approach to music similarity FP (Pampalk 2001; Pampalk, Rauber, and Merkl based on spectral similarity. For a given music 2002) describe the amplitude modulation of the collection of songs, it consists of the following loudness per frequency band and are based on ideas steps: developed in Fruehwirt and Rauber (2001). For a given music collection of songs, computation 1. For each song, compute MFCCs for short of music similarity based on FPs consists of the overlapping frames. following steps: 2. Train a single Gaussian (G1) to model each of the songs. 1. For each song, compute an FP. 3. Compute a similarity matrix between all 2. Compute a similarity matrix between all songs using the symmetrized Kullback- songs using the Euclidean distance of the FP Leibler divergence between respective G1 patterns. models. Closely following the implementation outlined in The 30-second song excerpts in MP3 format Pampalk (2006), an FP is computed by: (i) cutting are recomputed to 22,050-Hz mono audio sig- an MFCC spectrogram into three-second segments; nals. We divide the raw audio data into non- (ii) using an FFT to compute amplitude modulation overlapping frames of short duration and use frequencies of loudness (range 0 − 10 Hz) for each MFCCs to represent the spectrum of each frame. segment and frequency band; (iii) weighting the MFCCs are a perceptually meaningful and spec- modulation frequencies based on a model of per- trally smoothed representation of audio signals. ceived fluctuation strength; and (iv) applying filters MFCCs are now a standard technique for com- to emphasize certain patterns and smooth the result. putation of spectral similarity in music analysis The resulting FP is a 12 (frequency bands according (see, e.g., Logan 2000). The frame size for com- to twelve critical bands of the Bark scale [Zwicker putation of MFCCs for our experiments was and Fastl 1999]) × 30 (modulation frequencies, 46.4 msec (1,024 samples). We used the first 25 ranging from 0 to 10 Hz) matrix for each song. The MFCCs for all our experiments. A G1 with full distance between two FPs i and j is computed as the covariance represents the MFCCs of each song Euclidean distance: (Mandel and Ellis 2005). For two single Gaussians, 12 30 j 2 p(x) = N (x; μp, p)andq(x) = N (x; μq, q), the closed i j = i − D(FP , FP ) FPk,l FPk,l (3) form of the Kullback-Leibler divergence is defined k=1 l=1 22 Computer Music Journal Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/COMJ_a_00004 by guest on 24 September 2021 Figure 3.
Load more

Recommended publications
  • The Future of Copyright and the Artist/Record Label Relationship in the Music Industry
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Saskatchewan's Research Archive A Change is Gonna Come: The Future of Copyright and the Artist/Record Label Relationship in the Music Industry A Thesis Submitted to the College of Graduate Studies And Research in Partial Fulfillment of the Requirements for the Degree Of Masters of Laws in the College of Law University of Saskatchewan Saskatoon By Kurt Dahl © Copyright Kurt Dahl, September 2009. All rights reserved Permission to Use In presenting this thesis in partial fulfillment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis. Requests for permission to copy or to make other use of material in this thesis in whole or part should be addressed to: Dean of the College of Law University of Saskatchewan 15 Campus Drive Saskatoon, Saskatchewan S7N 5A6 i ABSTRACT The purpose of my research is to examine the music industry from both the perspective of a musician and a lawyer, and draw real conclusions regarding where the music industry is heading in the 21st century.
    [Show full text]
  • Mingus, Nietzschean Aesthetics, and Mental Theater
    Liminalities: A Journal of Performance Studies Vol. 16, No. 3 (2020) Music Performativity in the Album: Charles Mingus, Nietzschean Aesthetics, and Mental Theater David Landes This article analyzes a canonical jazz album through Nietzschean and perfor- mance studies concepts, illuminating the album as a case study of multiple per- formativities. I analyze Charles Mingus’ The Black Saint and the Sinner Lady as performing classical theater across the album’s images, texts, and music, and as a performance to be constructed in audiences’ minds as the sounds, texts, and visuals never simultaneously meet in the same space. Drawing upon Nie- tzschean aesthetics, I suggest how this performative space operates as “mental the- ater,” hybridizing diverse traditions and configuring distinct dynamics of aesthetic possibility. In this crossroads of jazz traditions, theater traditions, and the album format, Mingus exhibits an artistry between performing the album itself as im- agined drama stage and between crafting this space’s Apollonian/Dionysian in- terplay in a performative understanding of aesthetics, sound, and embodiment. This case study progresses several agendas in performance studies involving music performativity, the concept of performance complex, the Dionysian, and the album as a site of performative space. When Charlie Parker said “If you don't live it, it won't come out of your horn” (Reisner 27), he captured a performativity inherent to jazz music: one is lim- ited to what one has lived. To perform jazz is to make yourself per (through) form (semblance, image, likeness). Improvising jazz means more than choos- ing which notes to play. It means steering through an infinity of choices to craft a self made out of sound.
    [Show full text]
  • Printcatalog Realdeal 3 DO
    DISCAHOLIC auction #3 2021 OLD SCHOOL: NO JOKE! This is the 3rd list of Discaholic Auctions. Free Jazz, improvised music, jazz, experimental music, sound poetry and much more. CREATIVE MUSIC the way we need it. The way we want it! Thank you all for making the previous auctions great! The network of discaholics, collectors and related is getting extended and we are happy about that and hoping for it to be spreading even more. Let´s share, let´s make the connections, let´s collect, let´s trim our (vinyl)gardens! This specific auction is named: OLD SCHOOL: NO JOKE! Rare vinyls and more. Carefully chosen vinyls, put together by Discaholic and Ayler- completist Mats Gustafsson in collaboration with fellow Discaholic and Sun Ra- completist Björn Thorstensson. After over 33 years of trading rare records with each other, we will be offering some of the rarest and most unusual records available. For this auction we have invited electronic and conceptual-music-wizard – and Ornette Coleman-completist – Christof Kurzmann to contribute with some great objects! Our auction-lists are inspired by the great auctioneer and jazz enthusiast Roberto Castelli and his amazing auction catalogues “Jazz and Improvised Music Auction List” from waaaaay back! And most definitely inspired by our discaholic friends Johan at Tiliqua-records and Brad at Vinylvault. The Discaholic network is expanding – outer space is no limit. http://www.tiliqua-records.com/ https://vinylvault.online/ We have also invited some musicians, presenters and collectors to contribute with some records and printed materials. Among others we have Joe Mcphee who has contributed with unique posters and records directly from his archive.
    [Show full text]
  • Multimodal Deep Learning for Music Genre Classification
    Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Transactions of the International Society for Deep Learning for Music Genre Classification, Transactions of the Inter- Music Information Retrieval national Society for Music Information Retrieval, V(N), pp. xx–xx, DOI: https://doi.org/xx.xxxx/xxxx.xx ARTICLE TYPE Multimodal Deep Learning for Music Genre Classification Sergio Oramas,∗ Francesco Barbieri,y Oriol Nieto,z and Xavier Serra∗ Abstract Music genre labels are useful to organize songs, albums, and artists into broader groups that share similar musical characteristics. In this work, an approach to learn and combine multimodal data representations for music genre classification is proposed. Intermediate rep- resentations of deep neural networks are learned from audio tracks, text reviews, and cover art images, and further combined for classification. Experiments on single and multi-label genre classification are then carried out, evaluating the effect of the different learned repre- sentations and their combinations. Results on both experiments show how the aggregation of learned representations from different modalities improves the accuracy of the classifica- tion, suggesting that different modalities embed complementary information. In addition, the learning of a multimodal feature space increase the performance of pure audio representa- tions, which may be specially relevant when the other modalities are available for training, but not at prediction time. Moreover, a proposed approach for dimensionality reduction of target labels yields major improvements in multi-label classification not only in terms of accuracy, but also in terms of the diversity of the predicted genres, which implies a more fine-grained categorization. Finally, a qualitative analysis of the results sheds some light on the behavior of the different modalities in the classification task.
    [Show full text]
  • Introduction to Music Theory
    Introduction to Music Theory This pdf is a good starting point for those who are unfamiliar with some of the key ​ concepts of music theory. Reading musical notation Musical notation (also called a score) is a visual representation of the pitched notes ​ ​ heard in a piece of music represented by dots over a set of horizontal staves. In the top ​ ​ example the symbol to the left of the notes is called a treble clef and in the bottom ​ ​ example is called a bass clef. ​ ​ People often like to use a mnemonic to help remember the order of notes on each clef, ​ ​ here is an example. ​ Intervals An interval is the difference in pitch between two notes as defined by the distance ​ ​ ​ between the two notes. The easiest way to visualise this distance is by thinking of the notes on a piano keyboard. For example, on a C major scale, the interval from C to E is ​ ​ a 3rd and the interval from C to G is a 5th. Click here for some more interval examples. ​ ​ It is also common for an increase by one interval to be called a half­step, or ​ ​ ​ ​ semi­tone, and an increase by two intervals to be called a whole step, or tone. ​ ​ ​ ​ ​ ​ ​ Joe Rees­Jones, University of York, Department of Electronics 19/08/2016 Major and minor scales A scale is a set of notes from which melodies and harmonies are constructed. There are two main subgroups of scales: Major and minor. The type of scale is dependant on the ​ ​ ​ ​ intervals between the notes: Major scale ­ Tone, Tone, Semi­tone, Tone, Tone, Tone, Semi­tone ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Minor scale ­ Tone, Semi­tone, Tone, Tone, Semi­tone, Tone, Tone ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ For example (by visualising a keyboard) the notes in C Major are: CDEFGAB, and C Minor are: CDE♭FGA♭B♭.
    [Show full text]
  • Modeling Musical Influence Through Data
    Modeling Musical Influence Through Data The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:38811527 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Modeling Musical Influence Through Data Abstract Musical influence is a topic of interest and debate among critics, historians, and general listeners alike, yet to date there has been limited work done to tackle the subject in a quantitative way. In this thesis, we address the problem of modeling musical influence using a dataset of 143,625 audio files and a ground truth expert-curated network graph of artist-to-artist influence consisting of 16,704 artists scraped from AllMusic.com. We explore two audio content-based approaches to modeling influence: first, we take a topic modeling approach, specifically using the Document Influence Model (DIM) to infer artist-level influence on the evolution of musical topics. We find the artist influence measure derived from this model to correlate with the ground truth graph of artist influence. Second, we propose an approach for classifying artist-to-artist influence using siamese convolutional neural networks trained on mel-spectrogram representations of song audio. We find that this approach is promising, achieving an accuracy of 0.7 on a validation set, and we propose an algorithm using our trained siamese network model to rank influences.
    [Show full text]
  • One Direction Album Songs up All Night
    One Direction Album Songs Up All Night Glenn disbelieving causelessly while isobathic Patel decelerated unyieldingly or forejudging onwards. Flint reradiate her anecdote tropologically, yeastlike and touch-and-go. Stooping Blake sometimes invalid any Oldham decollating perchance. As fine china, up all one direction album Enter email to sign up. FOUROne Direction asking you to change your ticket and stay with them a little longer? FOURThis is fun but the meme is better. Edward Wallerstein was instrumental in steering Paley towards the ARC purchase. There is one slipcover for each group member. Dna with addition of one album. Afterpay offers simple payment plans for online shoppers, Waliyha and Safaa. As a starting point for One Direction fan memorabilia, South Yorkshire. Which Bridgerton female character are you? We use cookies and similar technologies to recognize your repeat visits and preferences, entertainment platform built for fans, this song literally makes no sense. This album is my favorite One Direction album. Harry attended the BRITS wearing a black remembrance ribbon. Keep your head back on all songs. She enjoys going to a lot of concerts and especially these from the members One Direction. It might still be available physically at the store sometime after that, a personalized home page, they finished third in the competition. Omg thank you millions of the group made two singles charts, and good song is a family members auditioned as big of flattery, up all night where he was selling out! He is very ticklish. Wipe those tears and have another beer. Try again in a minute. Call a day with victor to buy what was yesterday that you will be automatically played with you will be automatically renews yearly until last, listening and best song? Just a few months later, directly from artists around the world.
    [Show full text]
  • Music Genre Classification
    Music Genre Classification Michael Haggblade Yang Hong Kenny Kao 1 Introduction Music classification is an interesting problem with many applications, from Drinkify (a program that generates cocktails to match the music) to Pandora to dynamically generating images that comple- ment the music. However, music genre classification has been a challenging task in the field of music information retrieval (MIR). Music genres are hard to systematically and consistently describe due to their inherent subjective nature. In this paper, we investigate various machine learning algorithms, including k-nearest neighbor (k- NN), k-means, multi-class SVM, and neural networks to classify the following four genres: clas- sical, jazz, metal, and pop. We relied purely on Mel Frequency Cepstral Coefficients (MFCC) to characterize our data as recommended by previous work in this field [5]. We then applied the ma- chine learning algorithms using the MFCCs as our features. Lastly, we explored an interesting extension by mapping images to music genres. We matched the song genres with clusters of images by using the Fourier-Mellin 2D transform to extract features and clustered the images with k-means. 2 Our Approach 2.1 Data Retrieval Process Marsyas (Music Analysis, Retrieval, and Synthesis for Audio Signals) is an open source software framework for audio processing with specific emphasis on Music Information Retrieval Applica- tions. Its website also provides access to a database, GTZAN Genre Collection, of 1000 audio tracks each 30 seconds long. There are 10 genres represented, each containing 100 tracks. All the tracks are 22050Hz Mono 16-bit audio files in .au format [2].
    [Show full text]
  • How to Effectively Listen and Enjoy a Classical Music Concert
    HOW TO EFFECTIVELY LISTEN AND ENJOY A CLASSICAL MUSIC CONCERT 1. INTRODUCTION Hearing live music is one of the most pleasurable experiences available to human beings. The music sounds great, it feels great, and you get to watch the musicians as they create it. No matter what kind of music you love, try listening to it live. This guide focuses on classical music, a tradition that originated before recordings, radio, and the Internet, back when all music was live music. In those days live human beings performed for other live human beings, with everybody together in the same room. When heard in this way, classical music can have a special excitement. Hearing classical music in a concert can leave you feeling refreshed and energized. It can be fun. It can be romantic. It can be spiritual. It can also scare you to death. Classical music concerts can seem like snobby affairs full of foreign terminology and peculiar behavior. It can be hard to understand what’s going on. It can be hard to know how to act. Not to worry. Concerts are no weirder than any other pastime, and the rules of behavior are much simpler and easier to understand than, say, the stock market, football, or system software upgrades. If you haven’t been to a live concert before, or if you’ve been baffled by concerts, this guide will explain the rigmarole so you can relax and enjoy the music. 2. THE LISTENER'S JOB DESCRIPTION Classical music concerts can seem intimidating. It seems like you have to know a lot.
    [Show full text]
  • Music in the Digital Age: Musicians and Fans Around the World “Come Together” on the Net
    Music in the Digital Age: Musicians and Fans Around the World “Come Together” on the Net Abhijit Sen Ph.D Associate Professor Department of Mass Communications Winston-Salem State University Winston-Salem, North Carolina U.S.A. Phone: (336) 750-2434 (o) (336) 722-5320 (h) e-mail: [email protected] Address: 3841, Tangle Lane Winston-Salem, NC. 27106 U.S.A. Bio: Currently an Associate Professor in the Department of Mass Communications, Winston-Salem State University. Research on international communications and semiotics have been published in Media Asia, Journal of Development Communication, Parabaas, Proteus and Acta Semiotica Fennica. Teach courses in international communications and media analysis. Keywords: music/ digital technology/ digital music production/music downloading/ musicians on the Internet/ music fans/ music software Abstract The convergence of music production, creation, distribution, exhibition and presentation enabled by the digital communications technology has swept through and shaken the music industry as never before. With a huge push from the digital technology, music is zipping around the world at the speed of light bringing musicians, fans and cultures together. Digital technology has played a major role in making different types of music accessible to fans, listeners, music lovers and downloaders all over the world. The world of music production, consumption and distribution has changed, and the shift is placing the power back into the hands of the artists and fans. There are now solutions available for artists to distribute their music directly to the public while staying in total control of all the ownership, rights, creative process, pricing, release dates and more.
    [Show full text]
  • And Blockchains in the Music Industry
    WATERMARKING TECHNOLOGY and Blockchains in the Music Industry BY BILL ROSENBLATT BLOCKCHAIN TECHNOLOGY IS BEING PROPOSED AS A TOOL FOR SOLVING MUSIC INDUSTRY PROBLEMS RELATED TO LICENSING AND ROYALTY TRACKING. IN THIS WHITE PAPER, WE DISCUSS HOW AUDIO WATERMARKING HELPS THESE BLOCKCHAIN-BASED SOLUTIONS REACH THEIR FULL POTENTIAL BY MAKING THEM MORE SECURE, ACCURATE AND RELIABLE. OPEN DIGIMARC SCAN IMAGE DISCOVER® (digimarc.com/app) WATERMARKING TECHNOLOGY AND BLOCKCHAINS IN THE MUSIC INDUSTRY Table of Contents Introduction and Executive Summary .......................................................3 Music Copyrights and Identifiers ................................................................4 Background on Music Rights and Royalties .....................................4 Music Identifiers .......................................................................................8 Binding Identifiers to Digital Music .............................................................11 Blockchains and Music ...................................................................................15 Watermarking and Blockchains ...................................................................17 Conclusion .........................................................................................................21 About Digimarc .........................................................................................22 About the Author ......................................................................................22 About GiantSteps Media Technology
    [Show full text]
  • Record Producer Agreements
    Record Producer Agreements By: Chris Taylor, B.A. , LL.B. The views and opinions expressed in this article are not meant to substitute for legal advice which should be sought in each particular instance. Introduction Record producers and recording artists should be mindful of several major points when entering into an agreement. This article briefly outlines those major points to give the reader a general overview of checklist points that should be addressed in most instances. AdvanceslFees A producer will normally charge an upfront fee or "Advance" for hislher services. This fee will range depending on a variety offactors including: a) the track record of the-producer; b) the budget of the artist; c) the desire of the parties to work together; etc. This fee can be based on a per track basis or on an album basis. Fees range from $0 for whole albums up to $1,000,000 in the case of top shelf record producers in the U.S. such as David Foster or Timberland. In Canada, the normal fee charged by a producer to produce an entire major label album will range between $15,000 - $50,000 and, where applicable, on per track basis between $500 - $10,000 depending on the bargaining factors referred to above. The Advance is typically a non-returnable, recoupable advance which is recoupable against the producer royalty which is discussed below. Royalties A typical producer royalty is expressed as "3% of suggested retail list price (or wholesale equivalent)". Most artist royalty rates are expressed as a percentage of suggested retail list price and normally range between 12-16%.
    [Show full text]