Detecting sound inside a sound
I don't know if you are familiar with statistical signal processing and therefore will write my answer assuming that you are not. Everything I explain here is much better presented in any book about statistics. I would recommend Kay's book about detection theory. I first summarize your question by reformulating the 2 points you made, first in comprehensive English and then from a signal processing point of view. 

You have access to two signals:


The signal you are looking for. Be it music, speech, or whatever. The only important assumption about this signal is that you perfectly know it. We will name it the signal of interest and denote it $s$. Let's say that it consists in $N$ samples denoted $s[n]$, with $0 \le n &lt; N$.
A recording that contains the signal of interest as well as many other sources. The recording will thereafter be named the observation signal and denoted $x$. The other sources are all considered as noise in that they consist in anything but the signal of interest, and we will denote the noise $w$. The observation is longer than the signal of interest. Let's say it consists in $K$ samples denoted $x[k]$, with $K \ge N$ and $0 \le k &lt; K$. Same goes for $w$.


You said you know what you are looking for ($s$), but you don't know  how badly it is "drown" ($w$) in what you are observing ($x$). From this point, the first thing I would try is a matched filter (MF) approach. I can't say for sure it will work for your problem because you gave us nothing about $w$. What I can say for sure is that this is the easiest approach that is very likely to work, both from a theoretical and a programming point of view. If this does not work, then you can come back here again. The fun part will start from there :)

But for now, let's focus on the matched filter. $s$ is shorter that $x$, so you should be able to take $M$ successive snapshots of $x$ of the same length as $s$, each denoted $x_m$. From $s$ and those $M$ snapshots, you can construct a third signal $y$ of length $M$, with each sample defined as follows :
$$y[m] = \sum_{n=0}^{N-1}{x_m[n]s[n]}$$

If $s$ is really in $x$, then you may see a clear maximum in $y$, at the index $m$ of the snapshot that contains $s$. If not, two possible conclusions:


$s$ is not in $x$. But that should not be the case, according to the presentation you gave of your problem.
Either the "level" of $s$ is way too low as compared to $w$ (we talk about a bad signal-to-noise ratio), or the statistical properties of $w$ are making this more complicated. The fun starts in the latter case.