Well-performed audio narrations are a hallmark of captivating podcasts, explainer videos, radio stories, and movie trailers. To record these narrations, professional voiceover actors follow guidelines that describe how to use low-level vocal components---volume, pitch, timbre, and tempo---to deliver performances that emphasize important words while maintaining variety, flow and diction. Yet, these techniques are not well-known outside the professional voiceover community, especially among hobbyist producers looking to create their own narrations. We present Narration Coach, an interface that assists novice users in recording scripted narrations. As a user records her narration, our system synchronizes the takes to her script, provides text feedback about how well she is meeting the expert voiceover guidelines, and resynthesizes her recordings to help her hear how she can speak better.

Highly-produced audio stories often include musical scores that reflect the emotions of the speech. Yet, creating effective musical scores requires deep expertise in sound production and is time-consuming even for experts. We present a system and algorithm for re-sequencing music tracks to generate emotionally relevant music scores for audio stories. The user provides a speech track and music tracks and our system gathers emotion labels on the speech through hand-labeling, crowdsourcing, and automatic methods. We develop a constraint-based dynamic programming algorithm that uses these emotion labels to generate emotionally relevant musical scores. We demonstrate the effectiveness of our algorithm by generating 20 musical scores for audio stories and showing that crowd workers rank their overall quality significantly higher than stories without music.

Audio stories are an engaging form of communication that combine speech and music into compelling narratives. Existing audio editing tools force story producers to manipulate speech and music tracks via tedious, low-level waveform editing. In contrast, we present a set of tools that analyze the audio content of the speech and music and thereby allow producers to work at much higher level. Our tools address several challenges in creating audio stories, including (1) navigating and editing speech, (2) selecting appropriate music for the score, and (3) editing the music to complement the speech. Key features include a transcript-based speech editing tool that automatically propagates edits in the transcript text to the corresponding speech track; a music browser that supports searching based on emotion, tempo, key, or timbral similarity to other songs; and music retargeting tools that make it easy to combine sections of music with the speech. We have used our tools to create audio stories from a variety of raw speech sources, including scripted narratives, interviews and political speeches. Informal feedback from first-time users suggests that our tools are easy to learn and greatly facilitate the process of editing raw footage into a final story.

Audio producers often use musical underlays to emphasize key moments in spoken content and give listeners time to reflect on what was said. Yet, creating such underlays is time-consuming as producers must carefully (1) mark an emphasis point in the speech (2) select music with the appropriate style, (3) align the music with the emphasis point, and (4) adjust dynamics to produce a harmonious composition. We present UnderScore, a set of semi-automated tools designed to facilitate the creation of such underlays. The producer simply marks an emphasis point in the speech and selects a music track. UnderScore automatically refines, aligns and adjusts the speech and music to generate a high-quality underlay. UnderScore allows producers to focus on the high-level design of the underlay; they can quickly try out a variety of music and test different points of emphasis in the story. Amateur producers, who may lack the time or skills necessary to author underlays, can quickly add music to their stories. An informal evaluation of UnderScore suggests that it can produce high-quality underlays for a variety of examples while significantly reducing the time and effort required of radio producers.

Abstract The Virtual Museum is an interactive, electronic museum where users can move from room to room, and select any exhibit in a room for more detailed examination. The exhibits in the museum are educational, encompassing topics such as medicine, plant growth, the environment, and space. To facilitate interaction with the museum, a new method for navigating through a prerendered 3D space, and interacting with objects in that space has been developed, called ‘virtual navigation’. Virtual navigation employs real‐time video decompression for the display of, and interaction with, high‐quality computer animation. In addition, a representation for 3D objects in animated sequences is used which permits pixel‐accurate, frame‐accurate object picking, so that a viewer can select any 3D object to trigger movement within the 3D space, to examine an exhibit in animated form, or to play a digital movie or soundtrack. The use of precomputed video permits 3D navigation in a realistic‐looking space, without requiring special‐purpose graphics hardware.