School Leader Update, November 2002

Monthly newsletter produced by Iowa Department of Education

School Leader Update, October 2002

Monthly newsletter produced by Iowa Department of Education

School Leader Update, March 2005

Monthly newsletter produced by Iowa Department of Education

School Leader Update, May 2005

Monthly newsletter produced by Iowa Department of Education

Report of Recommendations to the Iowa Braille and Sight Saving School, June 30, 2004

State University Audit Report

School Leader Update, June 2005

Monthly newsletter produced by Iowa Department of Education

Mid-Iowa School Improvement Consortium, Independent Auditor's Reports, Basic Financial Statements and Schedule of Findings, June 30, 2004

Other Audit Reports - 28E Organizations

Report on Special Investigation of Before and After School Services, For the Period July 1, 2000 through December 31, 2004

Other Audit Reports - Special Investigation

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.

Perry Community School District, July 1, 2004 through March 31, 2005

Community School District Audit Report

School Leader Update, August 2005

Monthly newsletter produced by Iowa Department of Education

School Leader Update, July 2005

Monthly newsletter produced by Iowa Department of Education

High School Review Summary, 2005

A status report identifying technical assistance, policies, and promising practices to facilitate high school improvement statewide.

Report of Recommendations to the Iowa School for the Deaf, June 30, 2004

State University Audit Report - Iowa School for the Deaf

A two-stage approach for tonic identification in indian art music

In this paper we propose a new approach for tonic identification in Indian art music and present a proposal for acomplete iterative system for the same. Our method splits the task of tonic pitch identification into two stages. In the first stage, which is applicable to both vocal and instrumental music, we perform a multi-pitch analysis of the audio signal to identify the tonic pitch-class. Multi-pitch analysisallows us to take advantage of the drone sound, which constantlyreinforces the tonic. In the second stage we estimate the octave in which the tonic of the singer lies and is thusneeded only for the vocal performances. We analyse the predominant melody sung by the lead performer in order to establish the tonic octave. Both stages are individually evaluated on a sizable music collection and are shown toobtain a good accuracy. We also discuss the types of errors made by the method.Further, we present a proposal for a system that aims to incrementally utilize all the available data, both audio and metadata in order to identify the tonic pitch. It produces a tonic estimate and a confidence value, and is iterative in nature. At each iteration, more data is fed into the systemuntil the confidence value for the identified tonic is above a defined threshold. Rather than obtain high overall accuracy for our complete database, ultimately our goal is to develop a system which obtains very high accuracy on a subset of the database with maximum confidence.